Review of Self-Driving Taxi Research Papers

This document provides a review of four research papers on self-driving taxis, focusing on Waymo LLC's autonomous taxi service. The papers cover topics such as the safety of self-driving cars compared to human drivers, the design and user experience of autonomous taxi services, and attitudes towards autonomous on-demand mobility systems. The review includes an analysis of the methodology used, key findings, and the contributions of each paper.

Research paper review
of self-driving taxi

Table of Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
Research Paper 1 - Rage against the machine? Google's self-driving cars versus human drivers
................................................................................................................................................1
Research Paper 2 - Autonomous Taxi Service Design and User Experience........................4
Research Paper 3 - Self-Driving Cars: A Survey...................................................................7
Research Paper 4 - Attitudes Toward Autonomous on Demand Mobility.............................9
System: The Case of Self-Driving Taxi.................................................................................9
References:.....................................................................................................................................12

INTRODUCTION
The following discussions are made on an organization, Waymo LLC, it is one of the
leading international American autonomous driving technology development company based in
US in 2009. It offers the products and services like self-driving taxi services. It mainly uses the
technical concept of artificial intelligence in their systems and productions. Company is relied on
the concept of AI because they want to apply the machine learning using google brain in its self-
driving cars. They have used the concept of deep learning to teach their cars a certain thing such
as nature of the objects on roads and understanding that how they actually react and response to
senses they feel due to the concept of artificial intelligence (Badue, Guidolini, Carneiro and de
Paula Veronese, 2020). Therefore, this report covers the review of four research papers such as
Rage against the machine? Google's self-driving cars versus human drivers, Autonomous Taxi
Service Design and User Experience, Self-Driving Cars: A Survey and Attitudes Toward
Autonomous on Demand Mobility System: The Case of Self-Driving Taxi. All are reviewed in
context of the Waymo LLC company with the questions which are addressed are exploring
datasets, proposal, research Problems solved, methodology used, merits, demerits, aspects
covered in literature review, formulation and justification of the work, claims by the author,
justification of such claims, comparative analysis, findings, contribution, importance of findings,
scope of improvement, ways to improve, future dimensions and review.
MAIN BODY
Research Paper 1 - Rage against the machine? Google's self-driving cars versus human drivers
Proposal Methodology used
It describes the both disadvantages and
advantages in highway safety of self-driving
cars. Google has been inventing self-driving cars
and experimenting them under worker direction
on open roads since 2009. These transports have
been included in various crashes, and it is of
involvement that how this experiment
programme is different from manlike drivers in
Google car crashes were coded by variety
and intensity which is dependent on narration
discharged by Google. Crash taxation per
million vehicle miles travelled - VMT were
calculated for crashes took in an intense
manner adequately to be reportable to police.
These were differentiated with police-
reported crash taxation for manlike drivers.
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context of safety purposes. Crash forms also were differentiated.
Aspects covered in literature review
Literature review covers the aspects that are related to the comparison between the
efficiency of self-driven cars and human driven cars. Research is all about the concept of using
such transport in an efficient manner with a good safety in highway when using with in the form
of self-driven cars so that it can be encouraged more (Fan, Jiao, Ye, Yu, Pitas and Liu, 2019).
Formulation and justification of the work
Research has developed with the help of better formulation and justification of work by
using facts or figures and statistical data so that it can be justified in a more amended manner
such as the facts of Google crash events in autonomous mode that were judged to be police-
reportable. Observation of police-reportable Google crashes in autonomous mode in Mountain
View (Rage against the machine., 2017). Expected police-reportable Google crashes in
autonomous mode in Mountain View, based on Mountain View police-reported crashes and
related to VMT. Crash incident involvement rates per million VMT and 95% confidence
intervals by type Google autonomous mode and SHRP2 levels 1–3 combined.
Claims by the author
For example, “Blanco et al. (2016) found Google cars broadly have less crash taxation
than standard vehicles by examining the data from the Second Strategic Highway Research
Program (SHRP2) naturalistic driving study. The SHRP2 study concerned over 3000 active
drivers across six study sites throughout the United States. Each associate drove a vehicle fitted
out with detector and visual communication to evidenced all driving for up to 3 years’ time
period” (Raue, D'Ambrosio, Ward and Coughlin, 2019).
Justification of such claims
It can be justified as the references are used at the end of the research paper so that it can
be proved of such claims for example “Blanco, M., Atwood, J., Russell, S., Trimble, T.,
McClafferty, J., & Perez, M. (2016). Automated vehicle crash rate comparison using naturalistic
data. Final report. Blacksburg, VA: Virginia Tech Transportation Institute”.
Findings
Google cars has a very decreased charge of police - reportable crashes per million vehicle
miles travel than manlike drivers in Mountain Scene, but the variation was not factual important.
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The most ordinary kind of contact affecting Google cars were when they got rear-ended by
another manlike driven transport. Google cars has mutual obligations for solely unitary crash
(Manoharan, 2019).
Contribution
There are various authors who has contributed in building up this research paper such as
Billings, C. E. provided the concept of Human-centred aircraft automation and guidelines.
NASA Tech. Memorandum 103885. Moffett Field, CA: NASA Ames Research Centre.
Blanco, M., Atwood, J., Russell, S., Trimble, T., McClafferty, J., & Perez, M. described the
Automated vehicle crash rate comparison using naturalistic data. Blacksburg, VA: Virginia Tech
Transportation Institute. Davis, M & Co. briefed about National telephone survey of reported and
unreported motor vehicle crashes. Washington, DC: National Highway Traffic Safety
Administration. From Google, it is understood about Google self-driving car project. Mountain
View, CA: Google, Inc. Google clarified about Google self-driving car testing report on
disengagements of autonomous mode. Mountain View, CA: Google, Inc. National Highway
Traffic Safety Administration. Traffic Safety Facts has given a compilation of motor vehicle
crash data from the fatality analysis reporting system and the general estimates system.
Washington, DC: National Highway Traffic Safety Administration. SAE International described
about SAE International standard J3016. Schoettle, B., & Sivak, M. told about a preliminary
analysis of real-world crashes involving self-driving vehicles. Ann Arbor, MI: Univeristy of
Michigan Transportation Research Institute. Ulm, K has given the explanation of a simple
method to calculate the confidence interval of a standardized mortality ratio (SMR). American
Journal of Epidemiology, 131(2), 373–375. Waymo provided the report on autonomous mode
disengagements for Waymo self-driving vehicles in California. Mountain View, CA: Waymo.
Waymo.com (d). http://www.waymo.com. Eric R. Teoh is an elder mathematical statistician at
the Insurance Institute for Highway Safety. Since joining the Institute in 2006, Mr. Teoh has
conducted numerous studies quantifying the state of highway safety and identifying ways to
improve it. His research has focused on motorcycles, young drivers, large trucks and passenger
vehicle safety. Mr. Teoh holds a master's degree in math from the University of Alabama at
Birmingham, where he also attained his bachelor's degree. David G. Kidd is an elder research
scientist at the Insurance Institute for Highway Safety. Dr. Kidd surveys how drivers use various
applications, whether they are built into vehicles or brought into them, and the effects of those
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technologies on driver behaviour, cognition, performance and safety. He has authored papers on
topics such as distracted driving, safety belt use and rear visibility. Dr. Kidd attained a bachelor's
degree from Virginia Tech and an academic degree in psychology from George Mason
University.
Importance of findings
Such findings are important because it is essential to gain the knowledge about the safety
on highway while driving either in context of human-driven or can be in the terms of self-driving
concept as it promotes the more technical innovation so that easy travelling through the highway
can be maintained safe and secure (Yoganandhan, Subhash, Jothi and Mohanavel, 2020).
Scope of improvement
Results proposed the scope of improvement such as that the largely-automated transport
can execute more non - hazardous than manlike drivers in definite circumstances, but will go on
to be active in crashes with standard-driven cars. Moreover, sustainability is another major point
to improve so that it can be environment friendly.
Ways to improve and future dimensions
Results proposed ways such as Google self-driving cars, at the time of testing the system,
are much safer than standardised manlike - driven traveller conveyance. It is an important to look
that testing and experimenting is necessary before the final use so that minimised risk can be
occurred while implementing such systems with better sustainability measures (Wang, Wang,
Talbot and Schwager, 2019).
Research Paper 2 - Autonomous Taxi Service Design and User Experience
Proposal Methodology used
This study is anxious with the subsequent
glitches: (1) How should an independent taxi
facility be intended and field-tested if the
self-driving machinery is defective? (2) How
can defective self-driving machinery be
added by consuming facility flexibility?
 Customer journey map Touchpoint
Analysis Service blueprint
 Wizard of OZ
 Design thinking
 Survey In-depth interview
 User video analysis
Aspects covered in literature review
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Literature review covers the as independent - automobile machineries advance,
conservative taxi and car-sharing facilities are existence joint into a communal independent car
facility, and through this, it is predictable that the changeover to a new example of common
flexibility will commence. However, beforehand the full growth of machinery, it is essential to
precisely classify the wants of the facility’s operators and prepare customer-oriented project rules
consequently (Borenstein, Herkert and Miller, 2019).
Formulation and justification of the work
Research has developed with the help of better formulation and justification of work by
using facts or figures and statistical data so that it can be justified in a more amended manner
such as the facts of consumer ride chart and trace facts of manned or independent taxi. Examples
of probable difficult situations for each problem opinion (1st Place Solutions of Waymo Open
Dataset Challenge., 2020). Investigation procedures and approaches. Facility plan of
independent taxi. Interior and exterior situation of self-directed taxi such as divider boards,
filmed taking procedures, exhibition. User’s smartphone and the tablet in the car, Examination
procedure. Example of feeling bend dimension consequence. Images utilised to deliver site data
of computer-generated taxi stand. Expressive assessment of independent taxi facility and
Screenshots of passenger observing.
Claims by the author
For example, “SAV services have their disadvantages; however, first, there is always the
issue of safety? Even if technology advances, the successful market establishment of SAV
services depends on customers’ perception of their safety” (Tussyadiah, Zach, & Wang, 2017).
(Stilgoe, 2019)
Justification of such claims
It can be justified as the references are used at the end of the research paper so that it can
be proved of such claims for example, “Tussyadiah, I. P., Zach, F. J., & Wang, J. (2017).
Attitudes toward autonomous on demand mobility system: The case of self-driving taxi. In R.
Schegg & B. Stangl (Eds.), Information and communication technologies in tourism 2017 (pp.
755–766). Rome, Italy:Springer”.
Findings
This study implements a self-directed taxi service example through a Wizard of Oz
technique. Moreover, by leading pitch examinations with situations connecting a real taxi, this
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research inspects client discomfort opinions, and delivers a user-experience-based project
explanation for undertaking them (Gambi, Mueller and Fraser, 2019).
Contribution
There are various authors who has contributed in building up this research paper such as
Sangwon Kim is presently a product owner in Bungaejangter Inc., Korea. He acknowledged a
MS in K-School from Korea Advanced Institute of Science and Technology (KAIST). His
investigation benefits involve UX, HCI, and data analytics. Jennifer Jah Eun Chang is presently a
product designer at Kali Care in California, USA. She acknowledged a BS in Computer Science
and a MS in K-School from Korea Advanced Institute of Science and Technology (KAIST). Her
investigation interests include UI, UX and HCI. Hyun Ho Park is presently a PhD student in the
School of Business and Technology Management of Korea Advanced Institute of Science and
Technology (KAIST). His investigation interests involve invention approach and strategy. Seon
Uk Song is presently a CEO of Bee&Flower, Korea. He acknowledged a MS in K-School from
Korea Advanced Institute of Science and Technology (KAIST). His investigation interests
involve provision strategy. Chang Bae Cha is presently a COO of Jr Coding Lab, Korea. He
acknowledged a MS in K-School from Korea Advanced Institute of Science and Technology
(KAIST). His investigation interests involve provision design. Ji Won Kim acknowledged a MS
in K-School from Korea Advanced Institute of Science and Technology (KAIST). Her
investigation interests involve service design and block chain. Namwoo Kang is presently an
Assistant Professor of Department of Mechanical Systems Engineering at Sookmyung Women’s
University. He acknowledged a BS in Mechanical and Aerospace Engineering from Seoul
National University, a MS in Technology and Management from Seoul National University, and
a PhD in Design Science from University of Michigan. His investigation interests involve
artificial intelligence, design optimization, and HCI.
Importance of findings
Such findings are important because it is important to know about the user experience of
self-driving cars with the designing of cars with an attractive manner according to the user needs.
Reason behind this is that self-driving cars are important to gain trust and confidence from the
user and that is why it is essential to take care of their requirements (Chen, Chen, Zhang and
Zheng, 2019).
Ways to improve and future dimensions
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It is planned to design extra communication approaches that are extra advanced and
comprehensive to additional inspect the questions that are to be addressed when providing self-
directed taxi services. Particularly, it has gone to design and test the UI situated in the traveller
chair. The UI will permit the travellers to switch the chief tasks of services provided, monitor
outdoor environment in actual time, and allow numerous communications with the self-directed
taxis.
Research Paper 3 - Self-Driving Cars: A Survey
Proposal Methodology used
It is surveyed investigation on self-driving
cars available in the literature concentrating
on self-directed cars industrialised since the
DARPA challenges, which are armed with
an independence system that can be
characterised as SAE level 3 or advanced.
The building of the self-sufficiency system
of self-driving cars is characteristically
prearranged into the insight system and the
decision-making scheme. The insight system
is usually alienated into numerous
subsystems accountable for responsibilities
such as self-driving-car localization, still
problems charting, moving problems
discovery and following, street plotting,
traffic signalization discovery and gratitude,
among others. The decision-making system
is usually divided as well into numerous
subsystems accountable for responsibilities
such as way preparation, trail preparation,
performance assortment, gesture preparation,
and regulator. In this survey, it is presented
It is also reviewed investigation on applicable
approaches for insight and conclusion making.
Additionally, it is presented as thorough
explanation of the building of the independence
system of the self-driving car industrialised at the
Universidade Federal do Espı́rito Santo (UFES),
named Intelligent Autonomous Robotics
Automobile (IARA). Lastly, it is listed as
protuberant self-driving car investigation stages
industrialised by university and machinery
businesses, and stated in the television.
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that the typical construction of the self-
sufficiency scheme of self-driving cars.
Aspects covered in literature review
Aspects covered are distinctive Construction of Self-driving cars, self-driving cars’
insight, self-driving cars’ choice making, construction of the UFES’s car, IARA and self-driving
cars under growth in the business (Ndikumana, Tran Kim and Hong, 2020).
Formulation and justification of the work
Research has developed with the help of better formulation and justification of work by
using facts or figures and statistical data so that it can be justified in a more amended manner
such as the evidences of Summary of the distinctive ordered style of self-driving cars (2nd Place
Solution for Waymo Open Dataset Challenge., 2020). TSD means Traffic Signalization
Detection and MOT, Moving Objects Tracking. Presentation of Route Planning Techniques on
Western Europe. Figure of the response regulator. Block diagram of the software units that
instruments the software construction of IARA.
Claims by the author
For example, “Rohde et al. (2016) proposed a multilayer adaptive Monte Carlo
Localization (ML-AMCL) method that operates in combination with 3D point registration
algorithms” (Hewitt, Politis, Amanatidis and Sarkar, 2019).
Justification of such claims
It can be justified as the references are used at the end of the research paper so that it can
be proved of such claims for example, “Rohde, J., Jatzkowski, I., Mielenz, H., & Zöllner, J. M.
(2016). Vehicle pose estimation in cluttered urban environments using multilayer adaptive monte
carlo localization. In 2016 19th International Conference on Information Fusion (FUSION) (pp.
1774–1779). IEEE”.
Findings
In this paper, it is surveyed that the literature on self-driving cars concentrating on
investigation that has being verified in the actual world. Since the DARPA challenges of 2004,
2005 and 2007, a big body of investigation have donated to the present state of the self- driving
cars’ machinery. Though, abundantly still have to be completed to attain the business and
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academy area of creating SAE level 5 self-driving cars obtainable to the community at big
(Farag, 2019).
Contribution
There are various authors who has contributed in building up this research paper such as
Claudine Baduea, Rˆanik Guidolinia, Raphael Vivacqua Carneiroa, Pedro Azevedoa, Vinicius
Brito Cardosoa, Avelino Forechib, Luan Jesusa, Rodrigo Berriela, Thiago Paix˜aoc, Filipe
Mutzc, Lucas Veronesea, Thiago Oliveira-Santosa, Alberto Ferreira De Souzaa.
Importance of findings
A survey on self-driving is important because it depicts the future of technology and
comfort level of human beings along with the better trust and confidence on it. Therefore,
researching on designing, demand, technology, artificial intelligence has played an essential role
in the development of the automated car (Kim, Mokhtarian and Circella, 2020).
Scope of improvement
Improvement scope is described as the there must be an enhancement in the structure and
deep analysis of the market which are moving at a great pace in a market place and they are
highly required to be examined in an analytical manner for better research work (Sarcinelli,
Guidolini, Cardoso and Oliveira-Santos, 2019).
Research Paper 4 - Attitudes Toward Autonomous on Demand Mobility
System: The Case of Self-Driving Taxi
Proposal Methodology used
This study analysed the power of cognition
and holdings on practical application on
purpose to utilise self-driving taxi.
Dependent on a study with 325 residents in
the United States, this investigation
recovered degraded level of counter attitude
towards practical application such as
computers and robots and advanced level of
reliance in independent conveyance.
The form was apportioned via Amazon
Mechanical Turk, a
market for activity that needs manlike ability, in
July 2016. In
command to acquire prime information from
applicable responses, the study was
only successfully accessible to users with
support charge preceding 98%. This attempt
effected in 325 respondents. Responses are 60%
male, largely youngsters such as 58% below 35
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years old, largely college-educated such as 43%
have at least a Bachelor Degree and with family
financial gain less than US$ 40,000 for about
46%. Information were examined using
component determination, investigation of
discrepancy and hierarchical regression analysis.
Aspects covered in literature review
Topics covered in literature review are such that how the people react or act on the self-
driving cars in terms of their trust and confidence on such autonomous cars. They have
discovered both types of attitudes either positive or negative behaviour towards self-driving cars
(Acheampong and Cugurullo, 2019).
Formulation and justification of the work
Research has developed with the help of better formulation and justification of work by
using facts or figures and statistical data so that it can be justified in a more amended manner
such as the facts of attitude toward computers/robots, trust in self-driving taxi, correlation matrix
and results of regression analyses (Autonomous Taxi Service Design and User Experience.,
2021).
Claims by the author
For example, “The usage of self-governing conveyance is not only advantageous for its
sustainability through decreased ecological footmark of mobility that is commutation and
touristy but also for its efficiency in assets usage as it renders new possibilities for car sharing
models by connecting two resources: drivers and transports (Beiker, 2016; Hars, 2015; Lenz &
Fraedrich, 2016; Pavone, 2016)”. (Miörner and Trippl, 2019)
Justification of such claims
It can be justified as the references are used at the end of the research paper so that it can
be proved of such claims for example “Beiker, S. (2016). Implementation of an Automated
Mobility-on-Demand System. In M. Maurer et al. (eds.), Autonomous Driving. Berlin-
Heidelberg: Springer”.
Findings
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Founded areas in this research paper is negative attitude toward technology, trust in
technology and intention to use self-driving taxi (Farag and Saleh, 2019).
Contribution
Beiker, S provided the concept of Implementation of an Automated Mobility-on-Demand
System. In M. Maurer et al. (eds.), Autonomous Driving. Chafkin, M. provided the concept of
Uber’s First Self-Driving Fleet Arrives in Pittsburgh this Month. Bloomberg.
http://www.bloomberg.com/news/features/2016-08-18/uber-s-first-self-driving-fleet- arrives-in-
pittsburgh-this-month-is06r7on. Dietterich, T.G. & Horvitz, E.J. provided the concept of Rise of
Concerns about AI: Reflections and Directions. Communication of the ACM 58(10): 38-40.
Fagnant, D.J. & Kockelman, K. provided the concept of Preparing a Nation for Autonomous
Vehicles: Opportunities, Barriers, and Policy Recommendations for Capitalizing on Self-Driven
Vehicles. Advances in Hospitality & Tourism Research 2(1): 54-69. Hars, A. provided the
concept of Self-Driving Cars: The Digital Transformation of Mobility. In C. Linnhoff- Popien et
al. (Eds.), Marktplätze im Umbruch. Berlin-Heidelberg: Springer. Hsu, J. provided the concept
of 75% of U.S. Drivers Fear Self-Driving Cars, But It's an Easy Fear to Get Over. IEEE
Spectrum. http://spectrum.ieee.org/cars-that-think/transportation/self- driving/driverless-cars-
inspire-both-fear-and-hope. provided the concept of Lenz, B. & Fraedrich, E provided the
concept of New Mobility Concept and Autonomous Driving: The Potential for Change. In M.
Maurer et al. (eds.), Autonomous Driving. Berlin-Heidelberg: Springer. Mitchell, R. & Lien, T.
provided the concept of Uber is about to Start Giving Rides in Self-Driving Cars. Los Angeles
Times. Pavone, M. provided the concept of Autonomous Mobility-on-Demand Systems for
Future Urban Mobility. In M. Maurer et al. (eds.), Autonomous Driving. Berlin-Heidelberg:
Springer. Xiang, Z., Tussyadiah, I., & Buhalis, D provided the concept of Smart destinations:
Foundations, analytics, and applications. Journal of Destination Marketing & Management, 4(3):
143-144.
Importance of findings
It is important to find such attitudes and behaviours of the people towards self-driving
cars because it is essential to find out that if any invention or new development is being
promoted within a country then how the people gets affected through those innovations so that
accordingly they can mould their product as per the needs and requirements (Pusse and Klusch,
2019).
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Scope of improvement
Scope of improvement can be that in command to take off the barrier to acceptance of
self-governing on demand mobility method, it is adjudicatory for creators to pass on to the broad
public that the usage of independent conveyance would not diminish people’s roles that is
manlike drivers are no longer required, decreased worth of driving ability, but renders
possibilities for fresh functions that is new types of employment.
Ways to improve and future dimensions
Building reliance in self-driving cars amongst customers, particularly with respect to their
dependability, will also assure a high acceptance charge. While found inconsiderable to
determine purpose, spoken language used in communicating new practical application also plays
a function in forming customers’ perceptual experience. Referring to independent conveyance as
automaton cars, for example, may lead in customers perceiving them as more complicated, thus,
discouraging and little assistive (Muthalagu, Bolimera and Kalaichelvi, 2020).
References:
Books and Journals
Acheampong, R.A. and Cugurullo, F., 2019. Capturing the behavioural determinants behind the
adoption of autonomous vehicles: Conceptual frameworks and measurement models to
predict public transport, sharing and ownership trends of self-driving cars. Transportation
research part F: traffic psychology and behaviour, 62, pp.349-375.
Badue, C., Guidolini, R., Carneiro, R.V. and de Paula Veronese, L., 2020. Self-driving cars: A
survey. Expert Systems with Applications, p.113816.
Borenstein, J., Herkert, J.R. and Miller, K.W., 2019. Self-driving cars and engineering ethics:
The need for a system level analysis. Science and engineering ethics. 25(2). pp.383-398.
Chen, S., Chen, Y., Zhang, S. and Zheng, N., 2019. A novel integrated simulation and testing
platform for self-driving cars with hardware in the loop. IEEE Transactions on Intelligent
Vehicles. 4(3). pp.425-436.
Fan, R., Jiao, J., Ye, H., Yu, Y., Pitas, I. and Liu, M., 2019. Key ingredients of self-driving
cars. arXiv preprint arXiv:1906.02939.
Farag, W. and Saleh, Z., 2019. An advanced road-lanes finding scheme for self-driving cars.
Farag, W., 2019. Cloning safe driving behavior for self-driving cars using convolutional neural
networks. Recent Patents on Computer Science. 12(2). pp.120-127.
Gambi, A., Mueller, M. and Fraser, G., 2019, July. Automatically testing self-driving cars with
search-based procedural content generation. In Proceedings of the 28th ACM SIGSOFT
International Symposium on Software Testing and Analysis (pp. 318-328).
Hewitt, C., Politis, I., Amanatidis, T. and Sarkar, A., 2019, March. Assessing public perception
of self-driving cars: The autonomous vehicle acceptance model. In Proceedings of the
24th international conference on intelligent user interfaces (pp. 518-527).
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Kim, S.H., Mokhtarian, P.L. and Circella, G., 2020. How, and for whom, will activity patterns be
modified by self-driving cars? Expectations from the state of Georgia. Transportation
research part F: traffic psychology and behaviour, 70, pp.68-80.
Manoharan, S., 2019. An improved safety algorithm for artificial intelligence enabled processors
in self driving cars. Journal of Artificial Intelligence. 1(02). pp.95-104.
Miörner, J. and Trippl, M., 2019. Embracing the future: Path transformation and system
reconfiguration for self-driving cars in West Sweden. European Planning
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Muthalagu, R., Bolimera, A. and Kalaichelvi, V., 2020. Lane detection technique based on
perspective transformation and histogram analysis for self-driving cars. Computers &
Electrical Engineering, 85, p.106653.
Ndikumana, A., Tran, N.H., Kim, K.T. and Hong, C.S., 2020. Deep learning based caching for
self-driving cars in multi-access edge computing. IEEE Transactions on Intelligent
Transportation Systems.
Pusse, F. and Klusch, M., 2019, June. Hybrid online pomdp planning and deep reinforcement
learning for safer self-driving cars. In 2019 IEEE Intelligent Vehicles Symposium
(IV) (pp. 1013-1020). IEEE.
Raue, M., D'Ambrosio, L.A., Ward, C. and Coughlin, J.F., 2019. The influence of feelings while
driving regular cars on the perception and acceptance of self‐driving cars. Risk
analysis. 39(2). pp.358-374.
Sarcinelli, R., Guidolini, R., Cardoso and Oliveira-Santos, T., 2019. Handling pedestrians in self-
driving cars using image tracking and alternative path generation with Frenét
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Stilgoe, J., 2019. Self-driving cars will take a while to get right. Nature Machine
Intelligence. 1(5). pp.202-203.
Wang, M., Wang, Z., Talbot, J. and Schwager, M., 2019, June. Game Theoretic Planning for
Self-Driving Cars in Competitive Scenarios. In Robotics: Science and Systems.
Yoganandhan, A., Subhash, S.D., Jothi, J.H. and Mohanavel, V., 2020. Fundamentals and
development of self-driving cars. Materials Today: Proceedings, 33, pp.3303-3310.
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