Water quality monitoring at the dialysis units (DU) is essential to ensure an appropriate dialysis fluid quality and guarantee an optimal and safe dialysis treatment to patients. This paper aims to evaluate the effectiveness, economic and organizational impact of automation, digitalization and remote water quality monitoring, through a New Water Technology (NWT) at a hospital DU to produce dialysis water, compared to a Conventional Water Technology (CWT).A before-and-after study was carried out at the Hospital Clínic Barcelona. Data on CWT was collected during 1-year (control) and 7-month for the NWT (case). Data on water quality, resource use and unit cost were retrospective and prospectively collected. A comparative effectiveness analysis on the compliance rate of quality water parameters with the international guidelines between the NWT and the CWT was conducted. This was followed by a cost-minimization analysis and an organizational impact from the hospital perspective. An extensive deterministic sensitivity analysis was also performed.The NWT compared to the CWT showed no differences on effectiveness measured as the compliance rate on international requirements on water quality (100% vs. 100%), but the NWT yielded savings of 3,599 EUR/year compared to the CWT. The NWT offered more data accuracy (daily measures: 6 vs. 1 and missing data: 0 vs. 20 days/year), optimization of the DU employees’ workload (attendance to DU: 4 vs. 19 days/month) and workflow, through the remote and continuous monitoring, reliability of data and process regarding audits for quality control.While the compliance of international recommendations on continuous monitoring was performed with the CWT, the NWT was efficient compared to the CWT, mainly due to the travel time needed by the technical operator to attend the DU. These results were scalable to other economic contexts. Nonetheless, they should be taken with caution either when the NWT equipment/maintenance cost are largely increased, or the workforce involvement is diminished. End-stage renal disease (ESRD) is the stage where the patient needs a renal replacement therapy (RRT) to survive, which include dialysis and/or renal transplant. The European Renal Association (ERA) reported almost 700,000 people needs RTT with an incidence of 81,000 people/year in Europe . The groWater quality monitoring at the dialysis units (DU) is essential to ensure an appropriate dialysis fluid quality and guarantee an optimal dialysis treatment . With thThis study is focused on the water quality monitoring technology at the water plant of a hospital DU to produce dialysis water, comparing the Conventional Water Technology (CWT) with a New Water Technology (NWT), which is claimed to have potential added value through automated, digitalized and remote monitoring. This paper aims to evaluate the effectiveness, and the economic and organizational impact of the NWT in the water quality monitoring at a DU, compared to the CWT, to support health funding decision-making . There iThe water quality monitoring is currently being carried out on a typical offline / manual approach by the CWT. Allowing for some differences across countries, the CWT quality monitoring consists of one manual daily measure on a paper checklist by a technical operator (TO), followed by manual digitalization of the measurements. Hardness, chlorine and conductivity parameters are measured manually with reactive kits. The Colony Forming Units (CFU) and Endotoxin Units (EU) are measured through microbiological cultures. The chemical elements analysis is carried out using the most appropriate determination technique for each element, being each one detailed in ISO-23500-3. A monthly meeting including the TO, the head of nursery of the DU and the head of the DU is usually conducted to verify the data and report it to the quality control supervisor. The quality control supervisor quarterly issues a quality report. For the NWT, six daily measures are automatically and digitally recorded, instantly digitalized and sent to the data server. The TO can remotely access daily data from digital devices, having both detailed information as well as an overview of the quality parameters. Hardness, chlorine and conductivity parameters were measured through embedded sensors, while the CFU, EU and the chemical elements analysis were performed as for the CWT. The NWT integrates a system of warnings/alerts that TOs’ receives instantly if any parameter is near the threshold limit or out of standards, respectively . The automated, digitalized and remote monitoring with the NWT allows for an optimization of the workflow claiming to reduce the TO workload; a higher data accuracy and reliability as well as a reduction in days with absence of data; and a more efficient review during the monthly meetings and quality act reports. Although potential network fails and under/over triggering of warnings/alarms can occur, the NWT contains three pillars to ensure a high reliability: physical products , services (product related services and non-product related services) and digital solutions. This structure allows for a validation during the manufacturing process, during the dialysis unit’s setup and an annual regular check on performance which leads to a high reliability over 98% . A before-and-after study was carried out . The CWTdiasafe filters to achieve ultra-pure dialysate, located in the dialysis machines, are mandatory for these treatments in which the dialysate itself is used as a replacement solution. With these safety filters, it is unlikely to improve clinical results depending on water quality and/or dialysate [Although the water plant is an important and mandatory element of the dialysis process, once the dialysis fluid has been formed, two ialysate –16. Therialysate , were coA descriptive analysis (average/frequency and standard deviation) followed by statistical tests (t-test and chi-squared) on water quality and resource use variables comparing the CWT and the NWT was conducted. Neither sample size nor power calculation were performed since sample size of control and case groups were largely enough to compute the statistical tests.Using annual data, effectiveness and costs were evaluated for each technology. The effectiveness was measured as the compliance rate of the water quality parameters with the recommended standards by the international guidelines . The cosNWT: Annual aggregated cost of the NWT; CCWT: Annual aggregated cost of the CWT; ENWT: Compliance rate of the water quality parameters of the NWT; ECWT: Compliance rate of the water quality parameters of the CWTICER: Incremental Cost-Effectiveness Ratio; CSince both technologies met the international guidelines  requiremNWT: Annual aggregated cost of the NWT; CCWT: Annual aggregated cost of the CWTCMA: Cost-Minimization Analysis; CProspectively collected resource use that was not available for a whole year, and the subset that was not monthly collected (based on information retrieved from the HCB/FMC), were annually extrapolated assuming linearity. Besides, the working time of the DU employees was based on the opportunity cost, which assumed that the working time freed due to the NWT improvements may be spent in any other efficient task at DU, i.e. a 100% productivity of the DU employees was assumed. Therefore, even though the figures from human resources did not have a monetary impact to the hospital budget, they were accounted in economic terms and included in the total cost of each alternative. This analysis took a hospital perspective considering only direct costs incurred by HCB. Costs were not discounted due to the short time frame of the analysis (1-year).To assess robustness and transferability of the CMA results according to different guidelines’ requirements, health systems and economic contexts, extensive deterministic and scenarios sensitivity analyses were performed.A two-way sensitivity analysis on material resource use was performed. There was no consumption of supplementary task time related to the CWT and no filter replacements on the NWT. Given that these two figures were unlikely to occur, and therefore, an annual extrapolation would have been biased, the same level of consumption of these parameters for both technologies was assumed.Scenario 1 simulated a twofold improvement in the efficiency of water consumption. On the one hand, a water leak occurred during the NWT period, which was adjusted to the monthly average consumption. On the other hand, the water consumption of the NWT was extrapolated using only the period after an improvement in the water setting on the NWT (December-2019 to February-2020).Scenario 2a and 2b were built to offer different economic contexts where material and human resource unit cost may vary (scenario 2a-2b lower and higher unit costs were assumed respectively).Scenarios 3a to 3e simulated five scenarios that simultaneously varied the technology cost, its maintenance cost and its lifecycle, proposing a conservative, an intermediate and three favourable scenarios for the NWT.Since most of the DUs world-wide are composed by either a single DU or several DUs in the same physical location, different from this study, scenario 4 assumed that either DUs attached to the healthcare centre were in the same location or the healthcare centre had a single DU. Therefore, no TO travel time for both CWT and NWT was assumed.To simulate the compliance of international recommendations on continuous monitoring , scenariFinally, scenario 6 relaxed the assumption on the full-productivity of the DU employees reducing their productivity from 100% to 70%.Data was gathered from the database on resource use and technical variables , from an online survey on employees’ satisfaction, workflow and workload. Respondents’ inclusion criterion was those people involved in the water quality control at the DU . Respondents were contacted by email on May 2019, and to avoid bias according to the learning process of the NWT use, the same survey was answered online by the respondents on October 2019 and on January 2020, after the NWT installation. The study was conducted in a single DU and therefore the sample size was rather low (N = 5). Nonetheless, no other people were involved in the water quality monitoring at this DU. The survey contained a written informed consent and 11 questions comparing the NWT to the CWT in terms of time management, reliability of data collected and assessment of positive and negative aspects. Three questions formats were used: Likert scale, ranking and open questions. The survey is available in A descriptive analysis on the technical variables comparing both technologies was performed. Then, a descriptive analysis of the two survey answers was also conducted. Consistency and major differences between the two surveys were analysed.Although statistical significant differences on the water quality parameters between the NWT and the CWT were found, they were not considered technically relevant for the water quality since parameters for both technologies complied with the international guideline standards  . These savings were mainly caused by a significant decrease in the human  and the material resource use . As expected, these savings were offset with the incremental cost of the NWT equipment/maintenance compared to those of the CWT . The two-way sensitivity analysis and all scenarios except Scenario 3a and 4, showed savings ranging up to -21,218 EUR/year, where three measures per day instead of one needed to be performed, therefore presenting the NWT as an efficient technology compared to the CWT. In Scenario 3a, where conservative technology cost, its maintenance cost and its lifecycle for the NWT were considered the NWT did not yield savings, showing a low incremental cost (487 EUR/year). In Scenario 4, when either satellite DUs were in the same location or the healthcare centre had a single DU, the NWT did not longer yield savings showing an incremental cost of 1,918 EUR/year. Improvements in terms of the DU employees’ workflow and TO workload were observed. Network fails reported did not affect the data collection, but they affected the real-time data transmission and access. No Information Technology (IT) errors occurred during the study. Additionally, warnings related to the salt amount control were already expected since they were aimed at optimizing salt consumption. No alarms were reported. The NWT allows for 6 daily measurements, which offers more data reliability along with the automation and digitalization of water quality parameters reports. This led to guaranteeing daily availability of water quality information (missed information: 20 days/year for CWT vs. 0 days/year for NWT) and a significant reduction of TOs’ attendance to the DU. Particularly, the main TO has reduced the average visits by 80% (NWT: 4 days/month 4 vs. CWT:19 days/month). Furthermore, the Saturday TO was no longer needed. Tables Misunderstandings of the data reported by the NWT were found in the first survey. Therefore, training was provided and, on the second survey no issues in understanding were reported anymore. Results of the second survey are reported in All respondents were globally more satisfied with the NWT compared to the CWT. Likert scale questions showed that remote and continuous monitoring, reliability of data and process regarding audits for quality control were better valued for NWT than for CWT. With the NWT, daily workload was significantly improved for the two TOs and remained the same for the rest of the employees; while monthly meeting dynamism remained the same for two-third of the respondents. Respondents answered as first and second most important benefits of the NWT the data reliability and higher control due to higher frequency of monitoring. Regarding the most pressing issues of the NWT potential network failures/no data reception and potential IT errors related to the NWT  were ranked in the first and second place.Answers from open questions pointed out that the NWT offered the opportunity to easily collect both certain water quality parameters  and the traceability of the preventive/corrective actions, which the CWT did not offer. Survey results showed improvements depending on the employees’ position (survey results available upon request to authors).The NWT compared to the CWT provides with automation, digitalization and remote water quality monitoring for the first time worldwide in the clinical practice, performing an HTA. The HTA performed contains the three main pillars to health funding decision-making: evidence-based effectiveness, economic and organizational assessments, accordingly to the European Renal Best Practice Guidelines , to provCMA results were robust to the linearity and full-productivity assumptions; however they were sensitive to the NWT’s equipment/maintenance cost and its lifecycle, having or not satellite DUs, and stricter quality control standards framework Neither the saving of the base case nor the incremental cost of an increase of the NWT equipment/maintenance cost, represent a significant impact on the hospital DU’s budget. However, either the decrease of the NWT equipment/maintenance cost or a stricter quality control standards framework, would yield significant savings for the hospital DU’s budget.The continuous monitoring would also decrease the risk assessment related to undetected water contaminants. This fact is especially relevant for heavy equipment such as the water plant, which is not easy to be replaced, because it may lead to severe consequences, such as dialysis treatment delays. This saving would be relevant for a hospital DU’s budget, and would still comply with the higher standard requirements.This study is not free of limitations. The first is related to the difference in the number of observations between the control and case period, and the consequently linearity assumption of resource use variables. On the one hand, the low variability of retrospective data on quality water parameters allowed us to use 7 months of prospective data for the NWT as a representative annual sample. On the other hand, due to both the expected low monthly variability and the lack of a seasonality pattern of most of resource use variables, the linearity assumption was fair to compute annual figures. However, this assumption may not apply for two variables, the time spent on the supplementary tasks of the CWT and the filter replacement of the NWT. To test the robustness of the CMA, a two-way sensitivity analysis varying both resource use was conducted. The results showed that none variable had a significant impact on the results. Another weakness may be the unlikeliness that 100% productivity of the DU employees is met. To test this assumption, a sensitivity analysis varying the productivity to 70% was assumed, showing that the NWT still yielded savings. Other limitations are related to the sample size of the satisfaction and organisation impact survey.diasafe, potential patient outcomes may be observed in other healthcare centres, especially when considering interruption of treatment. Furthermore, TOs at HCB are trained by FMC providing with a better understanding and management of the CWT than in centres where TOs do not receive this training. Therefore, the advantages of the NWT may be larger in the latter centres. From the economic perspective, the NWT benefits may provide economies of scale for healthcare centres with several satellite DUs, and significantly increase savings when complying the international recommendations. Further research is encouraged in other clinical practice contexts to bring light on these assumptions.The automation, digitalization and remote monitoring of the NWT may provide potential benefits. Since HCB is a reference healthcare centre in the management of dialysis process using To conclude, the NWT guaranteed the standards compliance , 17 of tS1 TableCWT: Conventional Water Technology; NWT: New Water Technology.(DOCX)Click here for additional data file.S2 TableHCB: Hospital Clínic de Barcelona; FMC: Fresenius Medical Care; TO: Technical Operator; DU: dialysis unit; RO: Reverse Osmosis; n.a.: non-applicable.(DOCX)Click here for additional data file.S1 Survey(DOCX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.


A contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure—including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity. In the absence of an effective treatment, let alone a vaccine, the only possible mitigation strategies are non-pharmaceutical2. In the initial phase of the pandemic lockdown measures, implemented with various degrees of firmness in most countries, have proven effective in containing the epidemic3 and reducing the basic reproduction number 4. These are particularly crucial in the context of a disease that is transmitted by a large proportion of a-symptomatic and pre-symptomatic individuals, as studies of early outbreaks in China and Italy have indicated8. However, manual contact tracing can be a time-consuming and inefficient exercise, since models show that the probability of epidemic control decreases rapidly when not enough cases are ascertained via contact-tracing before the onset of symptoms9. Technology-based solutions have been proposed to automatise the tracking process, in the form of contact tracing smartphone apps. Exploiting the Bluetooth Low Energy technology, these applications can trace other smartphones coming into close contact for a period of time compatible with potential infection transmission10. Once an individual who uses the app discovers having been infected, all smartphones who have come into close contact receive a notification, signalling the potential exposure. Intense debate followed in the scientific community and among the public on the risks, especially to privacy, and effectiveness of the app-based solution11. A number of studies, using a range of methodologies, tried to establish the optimal adoption rate and the other necessary measures  required for the app to be effective in containing or suppressing the epidemic12. Analytical mathematical models show a generally optimistic stance, suggesting that instantaneous contact tracing, such as that afforded by the app-based solution, can lead to epidemic control13, if at least 60% adoption rate of the app is attained14.At the time of writing Coronavirus Disease 2019 (COVID-19) has caused just over 1,200,000 confirmed deaths in 216 countries worldwide15. Another model16, still in the development phase, seems to suggest that the app might not be effective at all, and other measures could be preferable, such as a random testing policy.Alternatively, other models highlight possible unintended consequences of the mitigation strategies. For example, adopting the app with an inadequate number of tests available may lead to epidemic control, but at the price of an unrealistic number of people having to isolateHere we present an agent-based model designed to explore the effectiveness of the app in containing or suppressing the epidemic. The model simulates the complex interplay between: (1) the portion of the population that uses the app, (2) the availability of testing, and (3) behavioural factors, such as the willingness to comply with self-isolation instructions. The model explicitly simulates realistic interactions and examines the different policy options on the table. Our modelling approach tries to balance the competing needs for flexibility and scalability, while retaining as much complexity and descriptiveness as possible.17. These models’ assumptions do not account for the heterogeneity that exists between individuals within the groups, and simplify the complexity of contact patterns, which take place in social networks that are important to understanding the course of an epidemic18.Modelling the effectiveness of mitigation strategies requires that we simulate them in parallel with the spread of the disease itself. Building on the principles of the SEIR approach, an agent-based model (ABM) was developed, simulating the spread of COVID-19 within the population of an urban area. Since the virus is transmitted through contacts between infected and susceptible individuals, in order to understand the dynamics of the spread it is essential to represent the multiple social networks that connect individuals within a population and determine patterns of contacts. A major weakness of traditional compartmental infection models is their aggregated nature. These models divide the population into homogeneous groups (compartments) in accordance with the state of the disease (SEIR), and assume disease transmission to occur as the infected group mixes with the susceptible group at certain ratesOur ABM intends to bridge this gap by modelling more realistic contact patterns that take place among heterogeneous agents interacting within a social network. Central to the agent-based approach is that each agent is represented in the model individually, with their specific characteristics (such as age and sex) and behaviour. Furthermore, interactions between agents are explicitly simulated.19, including household type, gender and age within geographical zones (Detailed Characteristic Sector 2011). The population represents the city of Glasgow, Scotland. A multi-layered social network links agents within the following social structures:household structure is created as follows: individuals who belong to households classed with the same type in the Census, and reside in the same locale, are linked together on the basis of age difference; single people below the age of 20 are assumed to live at home with one or two parents and siblings. Single people above the age of 20 are assumed to live independently, with a certain proportion co-habiting. Links of type ‘household’ are built among these agents.A relatives who don’t live in the same household (i.e. grandparents) are linked together.Family workplace sites are created, based on the distribution of workplace sizes in the city of Glasgow20. Active working-age agents are distributed among workplaces and linked to all co-workers at the same site as well as to a subset of colleagues who are assumed to be in closer, more frequent contact. Out of the working-age population, 13% of agents are assigned to customer-facing employment21, experiencing frequent contact with random agents of the population during work.Several friendship network links agents over 14 years of age, generated following the Barabasi-Albert model22 so that a scale-free network is produced, characterised by variation in number of friends per agent, with a median of 14 friends per agent, skewed towards similar age.A classes of maximum 30 children of the same age from the same zone and are linked together as classmates.Children between 6-17 years of age also belong to Using the social network we simulate daily contacts that take place between individuals within a population. Each type of social domain  is defined by frequency of encounters and the number of contacts per encounter . Hence, each day agents have random contacts with p = 1%  and COVID-19 detection tests. The CTA is distributed among a fraction of the population aged over 14. It stores in memory the ID of all other CTAs it came into contact with over the course of the previous 10 days. Infected agents who were tested positive can use the CTA to notify their contacts of possible exposure.Symptomatic agents who seek testing are assumed to get tested between 1 and 3 days after the onset of symptoms, and results are determined within a day. We assume that a fixed number of tests is available: as agents are tested the stocks decrease and restocking takes place daily. Agents seek testing when: (a) they feel symptoms, (b) they are notified of possible exposure to an infected agent. A notification is made to exposed agents when: (a) an agent feels symptoms or receives a positive test result and alerts all relatives; (b) a pupil tests positive and all classmates are made aware and quarantined; (c) a CTA user receives a positive test result and the app notifies all recorded contacts Fig. .30, of which 30% will seek COVID-19 testing. While these agents test negative, they contribute to the depletion of tests.To reflect the impact of influenza-like illness (ILI) on the testing system, we assume that over the course of any given week, 3.5% of the population experience ILIWhen agents self-isolate all their social ties are removed, except for household ties, as they are assumed to self-isolate at home. It is likely that some precautions are put in place between the infected agent and her household members, therefore we assume a 30% reduction in the probability of transmission within the household.31. We denote a parameter i to self-isolate when feeling symptoms of COVID-19. We also assume that agents who are notified by the CTA, but do not feel any symptoms, are less likely to self-isolate (than if they had symptoms) without testing. Therefore, their probability to self-isolate is reduced by factor Without the certainty that testing provides, surveys suggest that not all individuals will comply with self-isolation guidelines, both when feeling symptoms or when notified by the CTAentclass1pt{minima32. The distribution of daily contacts generated by the ABM holds a similar shape to the distribution in the survey. It is characterised by a lognormal body where most contacts are between 5-25, and a long tail of higher number of contacts with lower frequencies . The contact distribution in this scenario is presented in Fig. The core of our study explores the introduction of the CTA and the availability of testing into the baseline scenario of social distancing. We simulate the impact on viral spread of various combinations of: (1) proportion of CTA users in the population; 2) levels of testing capacity; (3) levels of compliance with self-isolation on the part of CTA users; (4) testing policy. Table  levels oTo evaluate differences in viral diffusion under alternative parameter combinations, for each simulated scenario we plot the overall proportion of the population infected at the peak and throughout the whole course of the epidemic. We use the baseline scenario of no tests and no app as point of reference to better understand the impact of the mitigations that are introduced.When testing is the only mitigation used (with no CTA), as testing capacity increases from 0 to 3%, overall infections decreases from 44 to 31% Fig. . A furthOnce the CTA is introduced, CTA users who are notified of having been in contact with an infected agent seek testing. When symptomatic agents are prioritised for testing, as the proportion of CTA users increases, overall infections throughout the epidemic decrease Fig. a,b, and When the testing capacity is restricted and symptomatic agents are not prioritised for testing, an increase in the proportion of CTA users does not always lead to a decrease in infections Fig. c,d. ThisIn general, the model suggests that the adoption of the CTA triggers two competing dynamics. On one hand, it informs agents and leads some of those infected to isolate even in the absence of a test: a crucial outcome, given the high proportion of a-symptomatic cases. On the other hand, it leads to a large and somewhat ‘inefficient’ depletion of tests, as several uninfected agents would likely seek testing after a notification from the app. The model shows that this counterproductive effect can be mitigated either with a testing policy that prioritises symptomatics cases, or with an overall increase in testing capacity. The scenarios with unlimited tests always show a substantial advantage in using the CTA.When compliance with self-isolation of CTA users is high, overall infections throughout the epidemic are only a few percentage points  agents notified by the CTA depletes the testing stock and prevents a number of symptomatic agents from being tested. In this case, the CTA may even produce an increase in the infection rate, especially if testing capacity is not sufficiently high relative to the number of CTA users.These phenomena that emerge in the simulation can offer generalisable policy-relevant insights. First, the model shows that to optimise the contribution of the CTA towards epidemic control an adequate testing capacity has to be in place. This capacity is dependent on the proportion of CTA users in the population. Second, the specific way in which testing resources are managed substantially affects the effectiveness of containment. The suggestion is that governments should implement the CTA in parallel with a substantial increase in testing capacity, and accurately plan the details of the testing policy, keeping in mind that the mere availability of the CTA substantially alters patterns of demand for testing.34.One of the aims of the model was to explore the impact of certain behavioural factors: we have shown that, even under conditions of low compliance of CTA users towards self-isolation, a significant decrease in the spread of the virus is achieved. Moreover, compliance with self-isolation can be enhanced by increasing the availability of testing to provide more certainty to exposed individuals regarding their infection state. Recent findings suggest that providing people with assurances about their livelihoods, by means of financial compensation, will increase compliance with self-isolationAs we demonstrated, in the more optimistic scenarios overall infections throughout the course of the epidemic are reduced to a third of the baseline scenario. The model shows that, when accompanied by social distancing, CTA and testing are likely to reduce the spread of the epidemic and contribute significantly to lowering the epidemic peak. This is especially crucial for the functioning and manageability of the healthcare system until a vaccine and other pharmaceutical treatments are developed. Furthermore, the model suggests that the effectiveness of the CTA is higher in situations of higher viral circulation . This potentially increases its importance in controlling the epidemic at the post-lockdown stage, where reopening of economic activity inevitably leads to higher infection rates.35. In such a case, the relative contribution of different social domains to the spread may differ and affect the extent to which CTA is employed. To address this issue, a sensitivity analysis for parameters affecting the viral spread is offered in the Supplementary Information section. The analysis shows that while the overall proportion of population infected may vary across parameter values, the findings regarding the impact of the CTA remain consistent for all scenarios. Another potential factor not implemented in our model is adoption rate differentials among demographic groups: the impact of such heterogeneity is still to be explored. A final caveat relates to our assumptions that the CTA functions “perfectly”  and that test results are accurate and returned within one day. In reality, shortcomings in these matters may impair the contact tracing strategy.The unprecedented nature of technology-based contact tracing, and the novelty of the disease itself, make it difficult to include, parametrise and validate every aspect of the model based on hard evidence, not least because such evidence often does not yet exist. As for any modelling exercise, the generated results rely on the model’s assumptions that simplify reality. We based the disease course parameters on emerging evidence as specified in Supplementary Table mentclass2pt{minimUK Table . With th36. While the CTA operates on a personal level by informing individuals of a possible risk of exposure, its general impact is at the societal level. By tracking exposed individuals and informing them to seek testing and self-isolate, transmission chains are interrupted, the spread of the virus is reduced, and so is the likelihood of infection for the whole population, including those who are not using the CTA.To conclude, we maintain that smartphone-based contact-tracing is a viable epidemic mitigation strategy, worth pursuing on the part of governments. The model suggests that, as larger fractions of society adopt the CTA, the spread of the virus is increasingly reduced, and, therefore, the benefits extend to the wider population. In principle, the CTA offers speed and cost efficiencies that can complement and extend traditional manual contact-tracing methods. In our view, the idea of technology-based contact-tracing should not be dismissed, especially not on the grounds that it may widen inequality by penalising those with limited access to the technology, or fail protect the elderly population who is less likely to adopt it. Paradoxically, users of the CTA do not benefit directly from doing so, since it only operates when an individual may already have been exposed to the virusSupplementary Information.


CHAMELEON is a computational melodic harmonization assistant. It can harmonize a given melody according to a number of independent harmonic idioms or blends between idioms based on principles of conceptual blending theory. Thus, the system is capable of offering a wealth of possible solutions and viewpoints for melodic harmonization. This study investigates how human creativity may be influenced by the use of CHAMELEON in a melodic harmonization task. Professional and novice music composers participated in an experiment where they were asked to harmonize two similar melodies under two different conditions: one with and one without computational support. A control group harmonized both melodies without computational assistance. The influence of the system was examined both behaviorally, by comparing metrics of user-experience, and in terms of the properties of the artifacts  that were created between the two experimental conditions. Results suggest that appreciation of the system was expertise-dependent . At the same time, users seemed to adopt more explorative strategies as a result of interaction with CHAMELEON based on the fact that the harmonizations created this way were more complex, diverse, and unexpected in comparison to the ones of the control group. The desire to define and measure human creativity , but in the augmented transitions matrix they are considered as separate chords. The, initially empty, two anti-diagonal blocks of the augmented transitions matrix are firstly filled with probability values belonging to transitions that are identical in both blended idioms . The probability value for each activated transition in the anti-diagonal blocks is the average of the two probabilities in the initial matrices. At a second stage, all pairs of the most common transitions on the initial idioms are blended  and valuable   or professional contemporary music composers  took part in the main experiment. The experimental procedure comprised two phases in a repeated-measures design. In phase one, participants were asked to harmonize the melody of a Greek traditional folk song called “Menexedes kai Zoumboulia” (melody A) in minor mode  also in minor mode see . An inspSelection of 35 Bach chorales from the Breitkopf edition. This set represents baroque homophonic harmonic style (seventeenth-eighteenth century).Selection of jazz standards from the Real Book (melodies with chord symbols), mainstream jazz harmony.The dataset of the Kostka-Payne corpus , minor pentatonic harmony.Fauxbourdon excerpts or short pieces .Selected modal homophonic chorales by Osiander, Praetorius, Scheidt, Hassler, Vulpius, Lasso, Walter, et al. Further categorization by mode is possible.Organum excerpts or short pieces (eleventh-twelfth centuries).The participants had the additional option to blend two selected styles in different tonalities, as, for instance, blend C minor in Bach Chorale style with E major in the Jazz style . Two voice layout options were also offered: (a) root position implementation of chords (that made reading the output easier); and (b) a statistical learning-based method that learned and first applied bass voice leading  and then intermediate voices were placed in closed position under the melody. The voice leading method is based on Cambouropoulos  and it eIn both experimental phases, participants filled in a 7-point Likert scale post-task questionnaire for user-experience evaluation whose questions represented the following metrics as defined by Kantosalo and Riihiaho : EnjoymeN = 22, mean age = 20.3, std age = 5, 12 female) took part in the control experiment. The directions were identical to the main experiment but participants were not asked to fill in questionnaires for user-experience evaluation. Only the two produced harmonizations were acquired. This design examines whether potential differences in the characteristics of the harmonizations acquired through the two experimental conditions could be attributed to the treatment  or resulted from other factors.The presentation order of the tasks in the main experiment remained fixed since the simple harmonization experience of melody A was viewed as a reference for the user-experience evaluation of the computationally supported harmonization of melody B. Therefore, a possible order effect needed to be taken into account for the analysis of extracted harmonic features (see section 2.3). To this end, a complementary control experiment was conducted whereby the two harmonizations were performed in the same fixed order  but without computational support for the second task. Twenty two students from the School of Music Studies of the Aristotle University of Thessaloniki .p. Greater SIE values indicate PCP distributions that are more uniform, which, in turn, indicates a richer variety of pitch content in the harmonization. Therefore, each harmonization was described by these three values, for each of which higher values indicated more complex harmonizations.The subjective user experience evaluation was complemented by a more objective assessment of harmonization characteristics based on computational extraction of harmonic features. Given that participants were free to use any type of harmonic palette, thus potentially avoiding tonal harmonic devices, harmonic content had to be captured using idiom-independent features of harmonic plurality. To this end, three different features based on the General Chord Type (GCT)  and harmonization 2 (h2) were devised as follows:Apart from capturing complexity we were also interested in quantifying the diversity of the harmonizations obtained for each experimental condition together with their deviance from typicality. A prerequisite for this was the ability to calculate distances between harmonizations. The feature values were used to estimate such distances between harmonizations (see numbered list below) which were subsequently compared between conditions. These distances were also exploited to construct geometric representations of the harmonizations through Multidimensional Scaling Analysis (MDS). Specifically, distance metrics between harmonization 1 . This metric incorporates the types or “qualities” of the chord labels—e.g., in jazz guitar-style chord notations, how many non-common X7 or Xm7 are included in the harmonizations under comparison.h1 and h2. Since the compared melodies are transposed to the same key, PCP vectors are unbiased in terms of tonality. This metric indicates the similarity of the overall harmonic content between two harmonizations. PCP information has proven efficient for categorizing music according to style  PCP vectors (distribution of pitch classes throughout the entire harmonization) extracted from z = −2, p = 0.051) between the two groups in the simple harmonization, albeit the p-value is marginally above the 0.05 threshold. On the other hand, there is a significant difference  in the computationally supported case. This constitutes a reversal of the picture between the two conditions. Indeed, in the simple harmonization composers featured a higher OCI , but in the computationally supported task their OCI declined substantially and was significantly lower than the corresponding OCI of students. It should be noted that the two conditions are not directly comparable at the general level due to the different number of questions involved in each post-task evaluation. These results already indicate an overall trend in the assessment of CHAMELEON between groups of participants and the following subsections will examine which specific questions are responsible for this general picture.The data of the post-task questionnaires were initially used to provide an indication of the overall creativity support offered by CHAMELEON as assessed by the two participant groups (students and composers). This was quantified by a metric that we refer to as the Overall Creativity Index (OCI) which was estimated as the average score of all the post-task questions for each participant. p/7 in this case (taking into account the 7 paired comparisons) according to a Bonferroni correction, then no effects would be identified. However, such an approach would increase the probability of falling into a type II error and rejecting an existing effect. According to the guidelines by Armstrong . On the contrary, the computationally supported condition featured a number of statistically significant differences. The responses did not pass the Shapiro-Wilk test for normality and as a result a non-parametric approach for median comparison was followed. Only one statistically significant difference was identified for the simple harmonization task and this was on the statement: “The harmonization of a given melody gave me the opportunity to be creative” with which composers agree more , as shown in All three harmonic features  that were calculated indicate an increase in the harmonic complexity of harmonizations when musicians are computationally assisted by CHAMELEON in comparison to the simple harmonization task. The higher values in the harmonization metrics reported above are an indication of increased complexity of the harmonizations produced with the assistance of CHAMELEON. We further calculated the distances between harmonizations based on these metrics to assess a potential difference in the divergence of the outcomes between the two experimental conditions. The distances between harmonizations were calculated as described in section 2.3.p = 0.05 level) for the simple harmonization task compared to the computationally supported one, thus indicating that the outcomes of the second condition were less homogeneous. p = 0.05 level) according to the Wilcoxon sign-rank tests. In this case, however, the control experiment also features higher medians in two out of the three metrics, although with smaller effect size for the number of GCTs. The direct comparison between groups shows that larger diversity of produced harmonizations may be supported only based on the number of GCTs and the SIE of the PCPs, and not on the number of GCT types.The vectors of distances for all metrics did not pass the Shapiro-Wilk test for normality and therefore their medians were compared using the non-parametric Wilcoxon rank-sum test. All comparisons were significantly lower  all kinds of comparisons between the control and the main experiment showed that the differences in harmonic complexity, diversity, and unexpectedness were greater for the main experiment compared to the control experiment. In other words, the increase of complexity, diversity, and unexpectedness observed in the computationally supported condition cannot be fully accounted for by the mere repetition of the task and should indeed be attributed—in some cases fully, in some partially—to the interaction with CHAMELEON.These results show that a system for computational support had a clear and quantifiable influence on the harmonizations created by human users. The question that arises is whether this observation could suggest an increase in the creativity of the users. The mere increase in complexity that was observed in the computationally supported artifacts is not sufficient to justify such a claim as the relationship between perceived value and complexity is not linear but is best modeled through the bell-shaped Wundt curve Heyduk, . This meAt this point, it is important to note that based on this experimental design it is hard to argue that computational support actually increased the creativity of the process . Furthermore, the press/environment and producer components of the four Ps framework  as primary material and drew upon harmonic information produced by CHAMELEON. These original works were presented in a concert in which the composers explained to the audience how they employed the creativity support system in their work. The creative exploitation of ideas and concepts suggested by CHAMELEON was evident in each of the short compositions and two indicative examples are presented below.One of the composers created five short variations each named after one CHAMELEON harmonic blend, namely: I. Bach chorales & Organum; II. Whole Tone & Kostka-Pane; III. Epirus & Jazz; IV. Kostka-Pane & Jazz; V. Modal chorales & Kostka-Pane; thus indicating the harmonic influence from each one of these blends. The first four measures of the second Whole Tone & Kostka-Pane variation are presented in These examples are only indicative of the many different harmonic possibilities and ideas suggested by CHAMELEON and adopted by the composers in their final works and they demonstrate the potential of human-computer collaborations in music creation. It is important to note that these influences were pointed out by the composers themselves and were not identified through our own analysis. On the contrary, and back to our main experiment, we recently presented a comparative analysis between the computationally supported harmonizations and the favored CHAMELEON examples of each participant that identified a number of different strategies for the creative exploitation of CHAMELEON  for the publication of any potentially identifiable images or data included in this article.AZ, MK-P, SK, and EC participated in the conception and design of the study. AZ and SK conducted the experiments. MK-P created the online version of CHAMELEON and extracted the harmonic features from the obtained melodic harmonizations. AZ performed the statistical analysis. All authors contributed to the writing of the paper, manuscript revision, and approval of the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In recent years, technological advancements have made a promising impact on the development of autonomous vehicles. The evolution of electric vehicles, development of state-of-the-art sensors, and advances in artificial intelligence have provided necessary tools for the academia and industry to develop the prototypes of autonomous vehicles that enhance the road safety and traffic efficiency. The increase in the deployment of sensors for the autonomous vehicle, make it less cost-effective to be utilized by the consumer. This work focuses on the development of full-stack autonomous vehicle using the limited amount of sensors suite. The architecture aspect of the autonomous vehicle is categorized into four layers that include sensor layer, perception layer, planning layer and control layer. In the sensor layer, the integration of exteroceptive and proprioceptive sensors on the autonomous vehicle are presented. The perception of the environment in term localization and detection using exteroceptive sensors are included in the perception layer. In the planning layer, algorithms for mission and motion planning are illustrated by incorporating the route information, velocity replanning and obstacle avoidance. The control layer constitutes lateral and longitudinal control for the autonomous vehicle. For the verification of the proposed system, the autonomous vehicle is tested in an unconstrained environment. The experimentation results show the efficacy of each module, including localization, object detection, mission and motion planning, obstacle avoidance, velocity replanning, lateral and longitudinal control. Further, in order to demonstrate the experimental validation and the application aspect of the autonomous vehicle, the proposed system is tested as an autonomous taxi service. The development of autonomous vehicles has seen a tremendous interest both from academia and industry. This significant progress in the autonomous vehicles has benefits in terms of mobilizing traffic capacity, vehicular emission and safety as a surrogate to conventional vehicles. Although the advance of autonomous vehicle is unassailable, the safety of the autonomous systems is of prime concern. The deployment of the autonomous vehicle in an urban environment is challenging and must accomplish the safety measures as standardized by SOTIF-ISO/PAS-21448 . However, the advancements in the vehicle dynamics, incorporation of deep learning for vision tasks and affordability of new sensor modalities have catalyzed the autonomous driving research and safety protocols.The concept of the autonomous vehicle has revolutionized for 100 years. In 1920s, “Phantom Auto” is articulated, which is the earliest attempt of autonomous vehicle . Many stBesides, the efforts have started in 1920s for the autonomous vehicle, the impact of DARPA autonomous driving competition has dramatically revolutionized the field of autonomous vehicles. The whole challenge is a big step towards autonomous driving, and the illustrious vehicles that are produced as an outcome of this competition are briefly discussed below. In 2005 the first DARPA competition winner, Stanley is created by the Stanford Racing Team in collaboration with Volkswagen Electronics Research Laboratory (ERL) . StanleyThe Society of Automobile Engineers (SAE) has specified six levels of automation, with level 0 corresponds to no automation features present in the vehicle to level 5 complete automation. The increase in automation level demands the deployment of robust sensor suite and algorithms. The three main subsystems that constitute to level 4 autonomous vehicle systems include perception, planning and control. The adoption of state-of-the-art sensors like Lidar, cameras, radar, Global Navigation and Satellite System (GNSS) in the perception module helps in utilizing the information about the surrounding environment. The planning module devises the path planning, obstacle avoidance and behaviour planning for the autonomous vehicle. Finally, the control module governs the course of action using longitudinal and lateral controllers.https://goo.gl/KdUyzA) provides the demonstration videos of our autonomous vehicle. The efficacy of our autonomous vehicle is experimentally verified by deploying it as an automated taxi service in the constrained environment. The proposed autonomous vehicle is composed of localization, perception, planning and control modules. The design of a distributed system and incorporation of robust algorithms enable the autonomous vehicle to perform efficiently. The fusion of sensor data for localization in map generation and navigation and also in perception module enable reliable object detection, recognition and classification in a dynamic environment. In the planning module, the optimal path is devised by considering the lane, obstacle information, and upon which velocity and behaviour planning are executed. Finally, based on the planning results, the control module performs the lateral and longitudinal control of the autonomous vehicle. In this work, the main contributions are as follows:(1)The proposed autonomous vehicle (car.Mlv.ai) is designed with constraint resources with minimal sensor suite compared to state-of-the-art vehicles. The cost-effectiveness of the proposed autonomous vehicle is one of the significant contributions of the proposed work.(2)The generation of 3D map for the localization of autonomous vehicle is built using the 32 channels Lidar with the auxiliary information for Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and odometry data from vehicle Controller Area Network (CAN) bus respectively.(3)For the autonomous vehicle’s perception, the fusion of exteroceptive sensors that include Lidar and camera is performed in the proposed work. In the fusion framework, both sensor’s object detection networks are trained and implemented in the Robot Operating System (ROS). The real-time efficacy of the detection network is evaluated by using TensorRT and deployed on the system.(4)A state-machine is designed that caters the mission and motion planning for the autonomous vehicle by incorporating the information of obstacles from the perception stack.(5)For longitudinal and lateral motion control, a customized controller, named Kiasoul-Socket is designed that caters throttle, brake, and steering of the autonomous vehicle by following the state-machine conditions for different real-world traffic scenarios, for instance, obstacle avoidance and obstacle stopping. The KIA Soul EV company does not provide this controller because of proprietary law.(6)Development of CAN-BUS shield for KIA Soul EV for acquiring the CAN messages from the vehicle for lateral and longitudinal control and providing the odometry data for the localization.(7)The proposed autonomous vehicle has experimentally validated in the application of autonomous taxi service.In this study, we have developed an autonomous vehicle using limited sensor suite as compared to autonomous vehicles discussed previously. The remainder of the paper is organized as follows—Autonomous driving systems are equipped with state-of-the-art sensors for robustness and reliability. In this work, we have used KIA Soul EV and retrofitted it by installing the exteroceptive and proprioceptive sensor for perception, navigation and internal vehicle monitoring tasks as shown in Exteroceptive sensors are used for the perception of the environment. The most common modalities that are used for the perception are camera and Lidar. In designing the autonomous vehicle, two FLIR Blackfly cameras along with one Velodyne Lidar have used for perceiving the environment. The precise inertial navigation of the autonomous vehicle is achieved by the integration of proprioceptive sensor in the sensors stack of the autonomous vehicle. For this purpose, the Novatel Global Navigation Satellite System (GNSS) is used which is a single device enclosing Global Positioning System (GPS) and Inertial Navigation System (INS) having an accuracy up to ±2 cm. The Novatel module contains inertial measurement unit (IMU) component, which consists of Fiber Optic Gyros (FOG) and Micro-Electromechanical System (MEMS) accelerometers. In addition, the odometry data is read from the vehicle Controller-Area-Network (CAN) bus. A CAN-BUS shield is used for reading the data from the CAN bus through OBD (On-board Diagnostic)-II connector. CAN-BUS shield is an MCP2515 CAN bus controller with SPI interface. It gives the data at a rate of 1 Mb/s.The design and development of control for an autonomous vehicle constitute towards the safe manoeuvring. In this respect, a drivekit which is an electronic controller specifically designed for drive-by-wire cars is used. It provides precise control by making an abstract layer on the top of the vehicle CAN bus, yet not affecting the vehicle own CAN bus. The drivekit provides its own CAN bus called Control-CAN which communicates with the vehicle CAN bus through a CAN Gateway for transferring and receiving the control signals. The modular approach is adapted to efficiently cater the computing consideration by using one server and laptop. In addition, two embedded systems which include raspberry Pi are also integrated into the computation stack. All the communication between the computers is carried over a gigabit Ethernet link with ROS running on the laptop and server. The vision sensors data is parsed to the server for the perception and planning of the environment while the Lidar and proprioceptive sensors data is processed by the laptop. The Kiasoul-socket controller is developed on one Raspberry Pi that generates the control command and communicates with the drivekit. The other Raspberry Pi is used for getting the GNSS data and then publish the data to be used by the laptop for localization and navigation purposes. The server acts as a ROS master, and all other other computing units communicate with it.V DC, lithium-ion polymer battery having the capacity of 27 kW h and power of 90 kW. All the sensors, computing units, and gigabit Ethernet module require AC power, so in order to meet that demand of AC power, an inverter is placed in the trunk of the car which provides necessary AC power to all equipment.The architecture of the autonomous vehicle is composed of four major layers, as illustrated in Localization is the foremost and essential task for the autonomy of the autonomous vehicle. Localization is the task of determining ego-position relative to the frame of reference in the dynamic environment. It facilitates any mobile robot to navigate the environment and avoid dangerous circumstances such as obstacles, collisions, and unsafe states. Typically, localization can be achieved using three techniques—utilizing GPS and IMU data, SLAM , and Priori map-based localization. Here, we have implemented Priori map-based localization on the core concept of correlation . It achiIn the context of autonomous vehicles, state-of-the-art localization systems utilize multi-modal point cloud matching techniques. The online point cloud obtained, which represents the environment’s situation, is matched against the prior point cloud map. The online scan is rotated and translated around its center to obtain position and orientation that gives a strong correlation between the two point clouds. The localized position of the sensor relative to the map gives the pose of the vehicle. The GPS and IMU data are used along with dead reckoning. The initial pose is estimated from GPS data. In summary, three main methods exist that use point cloud for mapping and localization. These methods include Normal Distribution Transform (NDT) , IteratiSLAM algorithm maps the environment by detecting similar features in two-point clouds and minimize their distance. It also detects loop closure . Kim et Biber et al. have introduced Normal distribution transform for matching a 2D laser scan . It tranHere, we employ the 3D normal distribution transform for mapping and localization using point cloud . Figure t is found by correlating the input point cloud data, and the reference point cloud data.R is the rotation matrix and For localization in the current environment, NDT matching is used. The matching of scan point cloud data with 3D map using NDT is a search problem. Equation  is used Equation .(2)E using vision camera and Lidar as sensor modalities. In the context of image-based object detection, the generalized object detection is evolved tremendously by consolidating the deep learning techniques. Moreover, the advancement in the computation hardware also makes it possible for the image-based deep learning object detection algorithms to be deployed in the autonomous driving perception framework. Besides, the increase in computing power, the image-based deep learning object detection algorithms must be in real-time and reliable for autonomous driving. For this purpose, we have used, the perception framework using single-stage object detection network rather than the region proposal detection framework. In this perception framework, YOLOv3 (You Only Look Once)  is used The affordability and availability of the camera makes it an elementary sensor modality for the perception tasks, but it has a drawback with illumination variation that affect the performance of object detection algorithms. In addition to illumination variation, the camera-based object detection operates in the projected image space in which it is difficult to estimate the scale of the scene. In order to overcome this hindrance, Lidar based object detection is used to bridge the gap between 2D image-based detection and project the outcome of the detection in the 3D frame. In literature, much work has been carried out to use Lidar data for the detection and classification of the object. Azam et al. have used multiple frames of Lidar to generate the CAD models, and then employs the convolution neural network architecture for the detection and classification of point cloud data ,41,42,43Algorithm 1: Ground plane removal based on angle and distance filteringFor the 3D object detection, the point cloud is filtered first by removing the ground plane before passing it to PointPillar network. The ground plane is removed by fitting a plane and applying a two-step filter. This filter uses angle and distance of two consecutive points to differentiate the ground points from vertical points. Algorithm 1 shows the pseudo-code for angle and distance-based filtering. The PointPillar network is composed of three main sub-modules (i) Pillar Feature Network, (ii) Backbone, and (iii) SSD Detection head. The filtered point cloud data is converted to pseudo-image for feature extraction by Pillar Feature network, which acts an encoder. The Backbone network learns the features of pseudo-image using a convolutional neural network, and finally, the SSD Detection Head is utilized to perform the 3D object detection. In addition, a unified Interacting-Multiple-Model with Unscented Kalman Filter and Joint Probability Data Association Filter (IMM-UK-JPDAF) tracker is used for tracking the 3D object detection .AlgoritIn the literature, researchers have used the calibration process for solving computer vision and robotics problems. In this work, we have used the camera-Lidar calibration as a bridge to project the 2D object detection results on the 3D frame. The camera-Lidar calibration has been divided into three main categories depending on the use of calibration objects—planar chessboard ,50, diffR is the rotation matrix, N is matrix formed by stacking all normals of grabbed chessboard in camera coordinate and M is the normal matrix grabbed in Lidar coordinate. The whole process of camera-Lidar calibration constitutes of three steps (i) Grabbing both sensor data, (ii) Extracting the points on the chessboard and (iii) Calibration optimization. First, the data is collected for 30 different poses of checkerboard by placing it in the range of both sensor’s field of view. In images, the checkerboard is extracted using OpenCV corner detection algorithm. For Lidar, the points are manually selected, that corresponds to the checkerboard location. The optimization is done on the camera intrinsic and extrinsic calibration parameters. The intrinsic camera calibration, which gives the position and orientation of each grabbed chessboard in the camera, are evaluated by the OpenCV PnP method . The extThe object detection in both sensor modalities and camera-Lidar calibration provides the foundation for the fusion of both sensor data. The fusion of both sensor modalities is carried out using the object detection matching. First, the point image is created by mapping the 3D detected object 2D frame. This point image, along with image-object detection bounding box and camera-Lidar calibration parameters are fed to reprojection module. In reprojection module, the mapping of 3D to 2D is evaluated and considered it a match if the overlapping of 3D object projection over the 2D detected object is greater than The experimental validation of our perception module is performed by implementing the whole stack in the ROS environment. The camera-based object detection is performed in the camera frame where YOLOv3 is implemented as ROS node and trained on the COCO dataset having a learning rate of Human driving addresses the problem of realistic traffic scenario and makes an optimal decision based on the current predicament because of traffic congestion or unwanted circumstances. An autonomous vehicle while operating in these scenarios must take an indispensable decision for which the planning module comes into play. The planning module of our autonomous vehicle integrates mission and motion planning for optimal trajectory generation, and behavior planning. The path is generated using the waypoints which are composed of pose and velocities obtained from the localization. These waypoints serve as direction to follow for the autonomous vehicle. These waypoints are processed by the lane planner (mission planning) and depending on the traffic conditions; these waypoints are manipulated. The mission planning of the autonomous vehicle is designed as a rule-based planner that incorporates the lane information. The lane planner receives multiple routes in the form of waypoints  and using the lane information it calculates the nearest path from the current location. Based on the current route, it detects the left and right lane using the lane change flag. The optimal route is selected for the autonomous vehicle and published as optimal waypoints for the motion planning for path planning.The motion planning module deals with path generation and path planning by incorporating the information of lane, obstacle detection, and velocities.Planner for the planning can be grouped into the global planner and local planner. The global planner servers as the path to be followed by the autonomous vehicle over the entire coarse. The local planner comprises of trajectory planning and behaviour planning in the form of obstacle avoidance/stopping and velocity replanning.Smooth trajectory planning and obstacle avoidance are the primary purpose of path planning and can be consolidated in the local planner. In the context of path planning for the autonomous vehicle, the local planner uses the modified A-star (A*) graph traversal algorithm for the path-finding with the addition of obstacle avoidance and velocity replanning . In our The obstacle avoidance and velocity replanning are implemented as a state-machine by incorporating the information from perception module and lane planner. The planning module of our autonomous vehicle integrates mission and motion planning. The autonomous vehicle’s mission planning is designed as a rule-based planner that incorporates the information of the lane. The motion planning is implemented as a state-machine employing obstacle avoidance and velocity replanning. The We have conducted the experimentation to illustrate the quantitative evaluation of proposed autonomous vehicle. In the first experimentation we have provided the quantitative evaluation of vehicle stopping upon the detection of obstacle. ’What should I do?’ acknowledge the autonomous vehicle’s concern to execute the commands received from the planning module by providing the necessary inputs to the actuators that will generate the desired motions. The planning layer generates the velocity and trajectory profile which autonomous vehicle requires to follow. The controller for the autonomous vehicle is designed by keeping in view the safety and stability concerns. In the context of the autonomous vehicle, modelling of vehicle dynamics plays an essential part in selecting and designing the optimal controller. The vehicle dynamics model constitutes the longitudinal and lateral model of vehicle . These ve corresponds to the error signal. In our case, for the autonomous vehicle, the velocity profile of longitudinal control constitutes of brake, torque, and throttle. For the vehicle to follow the velocity profile, a customized PID controller is designed to minimize the difference between current and target values of the torque, brake, and throttle. The current values are obtained from the vehicle CAN bus, whereas the target values are taken from the planning layer. The proprietary laws of KIA Company do not share the CAN bus IDs, and the CAN bus IDs are empirically found using CAN sniffer (https://github.com/linux-can/can-utils). For each throttle, torque and brake a PID controller is designed with two sets of control parameters. This framework provides flexibility to control the vehicle on high and low torque for the steering wheel, and also the high and low speed for throttle/brake.The generation of velocity profile for the autonomous vehicle is performed by the longitudinal control. In classical control, feedback controllers are used in several applications because they are actively compensating for the deviation between the measured and desired responses. Proportional-Integral-Derivative (PID) is the feedback controller that is extensively used in the process control industry ,64. The The framework for the PID controller is shown in The path tracking for the autonomous vehicle incorporates the design of lateral control. In our case, two path tracking algorithms are implemented for the lateral control, (i) Pure Pursuit, and (ii) Model Predictive Control (MPC)-based path follower.L gives the look-ahead distance, and goal point followed by the algorithm is denoted by Pure Pursuit is a path tracking algorithm which estimates the curvature from the current location to the goal location. The goal position is re-calculated such that it is constant distance ahead of the current vehicle location. In summary, Pure Pursuit chases a changing goal position some distance ahead, which is called look-ahead distance. o in Equation  is another approach for designing controllers dealing with multivariable constrained control problems . In MPC,The model is discretized, where The lateral and longitudinal control is evaluated and experimented on the autonomous vehicle. The longitudinal control generates torque, throttle, and brake commands for the autonomous vehicle. The PID controller is employed, and its optimization parameters are given by Equation . These tn method , and . The 3D map is moreover filtered using voxel filtering. The 3D map is built in Lidar reference frame; this 3D map is then transformed to world coordinate using the GPS data. The transformation is calculated by identifying key landmarks in point cloud map, and world coordinate from GPS data.The localization of the autonomous car is based on the 3D point cloud map. The NDT mapping is used to develop the 3D map. The data is collected using 32 channels Lidar. As the Lidar scans consist of In order to get precise GPS and IMU data, we have installed a Novatel GNSS module in the autonomous vehicle. Novatel company provides software support for windows, and only a few open-source drivers are available for Linux operating system. Since our autonomous car is using ROS as middleware for the development of software stack; the GPS and IMU data are needed to be in the ROS format. In order to cater to this issue a Linux based driver was developed that runs the Real-time kinematic (RTK) client and convert the data to ROS format. The raspberry pi is attached directly to the GNSS module through USB, and it publishes the data to ROS master over Ethernet. Odometry data provides vital information for vehicle control. Here, the odometry data was obtained from CAN bus instead of IMU. The major disadvantage of using IMU is that it suffers from accumulated error. The velocity from IMU is continuously integrating over time, and small error are accumulated over time, and this creates the problem of drift, whereas velocity from CAN bus is accurate. Drivekit does not provide odometry data. We have used CAN-BUS shield to read messages from CAN bus. We decoded CAN message corresponding to odometry data empirically because the ID’s of the CAN bus is proprietary information and the automotive company does not provide this information.The detection module consists of fusion of exteroceptive sensor data by performing object detection using and Lidar, and camera. All the processing is needed to be performed in real-time. The Lidar has a frame rate of 15 Hz, which set a maximum limit for processing the raw data. The detection of the object in images is done by using YOLOv3 which gives processing frame rate of 15 Hz. We have opted to use PointPillars for detecting the objects in Lidar. An Intel core i7 processor with TITANX GPU having 47GB memory, is used for detection and fusion module. The fusion of data is computationally inexpensive as only detected object bounding boxes are transformed into a common frame. The data fusion also provides the reliability of data by compensating the weakness of one sensor with the other. The camera provides spatial information, and the Lidar receives depth information, so the fusion of these two gives both the spatial and depth information about the surrounding environment. Another major challenge is to compensate for the computation power required by the fusion. In our scenario, we split the process into two systems. The Lidar-based detection, and fusion is performed on one system that runs the ROS-master. The other system is laptop which also works as a front-end for the user, and also performs the object detection in images, and publishes the detected object message to the ROS-master. In this distributed system, the latency, and computation issues are resolved.The parameter tuning for planning module includes stopping distance, stopping acceleration limit, and obstacle avoidance velocity limit. All the aforementioned parameters are tuned manually by testing in real-time, and are optimized for our autonomous vehicle. A customized PID controller is designed for our autonomous vehicle. It consists of a PID controller for each torque, brake, and throttle. The PID parameters are sensitive, and they were tuned for specific maximum speed using the Cohen-Coon method and genetic algorithm. There are three ranges of speed, 0–10 km/h, 10–30 km/h and 30–60 km/h. The obtained parameters were extensively tested.This study developed an autonomous vehicle with a minimal sensor suite and designed a distributed architecture for the proposed autonomous vehicle. The autonomous vehicle architecture constitutes the sensors layer, perception layer, planning layer, and control layer. The integration of exteroceptive and proprioceptive sensors, data fusion of exteroceptive sensor for the perception of the environment, and state-machine design for optimal trajectory generation by complying the traffic rules and a customized controller for longitudinal and lateral control are experimentally evaluated in an autonomous taxi service. The autonomous vehicle is tested in several scenarios and has successfully performed stopping at the obstacle in the two-way single-lane road, obstacle avoidance, complying with traffic rules, collision avoidance, and keeping in the lane. The experimental analysis of proposed modules in each layer is evaluated in a real-time operating system. Also, the system’s latency corresponding to each ROS topic is evaluated in the proposed work.Future work includes the integration of ultrasonic sensors to detect small objects near the vehicle and the autonomous parking. Furthermore, audio-visual information inclusion to detect an emergency vehicle and ego-lane change is part of future work. In addition, the voice activation of receiving the command from the passenger for the destination is included in the future work.