Autonomous Cars

In this study, a deep learning-based model has been proposed so that highway vehicles with autonomous driving capability can recognize the lanes they are in and determine their position on the highway while driving. 7000 highway images with different weather conditions were used to create this deep learning model. During the model training, the values of the training parameters were renewed, experiments were made and the error rate of the created model was tried to be minimized. As a result, in this proposed deep learning-based model, lanes on the highway were detected with an error rate of 0.09%.

Automobiles are the technological innovation that has most influenced the 20th century’s social, economic, and legal structure. More than 100 years after the introduction of the auto, in an era again displaying extensive technological change, vehicular traffic is set to undergo major, radical, abrupt transformations. This book describes the scenarios and prospects stemming from the emergence of highly automated, connected driving. The key contribution of artificial intelligence will usher in an unprecedented mobility paradigm combining efficiency, safety, and environmental sustainability. Current and forthcoming problems, challenges, and at times tragic choices associated with the imminent “mobility revolution” are analyzed in the light of the Law and Economics method, of which the author Guido Calabresi (Yale University) is one of the founding fathers and (for over the past 50 years) a visionary scholar, and in the perspective of a dialogue between law and technology conducted by Enrico Al Mureden (University of Bologna) - professor of Civil Law and Product Safety, Product Liability and Automotive - through a systematic reading of the traditional categories of civil law and the harmonized technical standardization that dominates the automotive sector globally.

Presentation of the book by Enrico Al Mureden
2.12.2021 - 10 am - Geneva University

Autonomous vehicle that can drive autonomously without human interference using cameras and sensors​ From the innovations of this car intelligent suspension system​ Fuel consumption optimization system​ Accident prevention system​ AWD propulsion system​ Mantis processing system, which utilizes two fish-eye cameras and servo motors, can increase vehicle visibility and increase driving safety.​ Removable fourwheel drive system

This idea in the 24th FIRA Robot World Cup 2019 Changwon Korea (Technical Challenge Autonomous Cars) from the Federation of international Robot sport AssociationIt has won the first place in the world

— In this paper we propose a trajectory planning approach for autonomous vehicles on structured road maps. Thererfore we are using the well-known A * optimal path planning algorithm. We generate a safe optimal trajectory through a three-dimensional graph, considering the two-dimensional position and time. (1) The graph is generated dynamically with fixed time differences and flexible distances between nodes, based on the vehicle's velocity, using a structured road map. (2) Furthermore the position of dynamic obstacles is predicted over time along the road lanes. The proposed Flexible Unit A * (FU-A *) algorithm was tested for real-time applications with execution times of less than 50 ms on the car's main computer. The feasibility and reliability of FU-A * is validated by implementing on simulated autonomous car of Freie university " MadeInGermany " using the roadmap of Tempelhof, Berlin.

The field of autonomous car is of great interest to researchers, and much has been accomplished in this area, of which this paper presents a detailed chronology and developing a self driving car prototype. This paper can help one to understand the trends in autonomous vehicle technology and we also build a prototype of a self driving car .The car is built using all the essential blocks including the navigation, compass, sensor, odometer and classifier blocks and we also see how these blocks communicate with each other to make the car autonomous. In this project we explore many technical fields including Artificial Intelligence, Deep Learning and neural networks, Database Management, Android app development and use of microcontrollers to build our self-driving car. The implementation of this idea into the real world would result in a vast number of advantages. Prevention of accidents, a huge revolution in cost of transportation, a god solution to death of 1.3 million people worldwide death due to car accidents, are few advantages of this idea.

Localization and mapping at the same time is one of our problems in this century, specially in autonomous vehicles. The project revolves around implementing a SLAM algorithm on a two-wheeled robot equipped with a camera and sensor. To do this, we use the robot operating system known as ROS to simulate our robot in the real world. Our robot configuration was studied in detail using Gmapping on ROS.From the configuration we study, we believe that the combination of Kinect and sensor will be the best choice for our sensor, which is supported by empirical evidence.

This paper deals with a 2D unity simulation of an autonomous car learning to drive in a simplified environment containing only lane and static obstacles. Learning is performed using the neural network and evolutionary algorithm. The cars navigate by evolving themselves from the previous generation. The neural network used in this paper is standard, fully connected, feed forward neural network. The cars are steered by a feed forward Neural Network. The weights of the network are trained using a modified genetic algorithm.

Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Laser range finder and stereo vision have been successfully used for obstacle detection, mapping and localization to solve the autonomous driving problem. Unfortunately, Light Detection and Ranging (LIDARs) are very expensive sensors and stereo vision requires powerful dedicated hardware to process the cameras information. In this context, this article presents a low-cost architecture of sensors and data fusion algorithm capable of autonomous driving in narrow two-way roads. Our approach exploits a combination of a short-range visual lane marking detector and a dead reckoning system to build a long and precise perception of the la...

In this study, a deep learning-based model has been proposed so that highway vehicles with autonomous driving capability can recognize the lanes they are in and determine their position on the highway while driving. 7000 highway images with different weather conditions were used to create this deep learning model. During the model training, the values of the training parameters were renewed, experiments were made and the error rate of the created model was tried to be minimized. As a result, in this proposed deep learning-based model, lanes on the highway were detected with an error rate of 0.09%.

This article explores the tension between the freedom a self-driving car offers and privacy considerations. Studies of the automobile's impact on the environment, public health, noise, planning, and development, as well as its appearance in, and inspiration of, popular culture are easy to find. In the field of communication, research on cars as a medium or as a site of communication often falls into the domain of mobilities, as defined by John Urry (2007). Mobilities consider not just the travel of people and objects through space, but also the imaginative, virtual, and communicative travel of messages between people and things. Julia Hildebrand (2017) notes the relationship of mobilities to media ecology and argues that we can understand the exploration of converging media and mobility entities as transportation-information-communication technologies. Furthermore, imaginaries of the autonomous car promote greater freedom for drivers while raising concerns with the necessity of increased connectivity. The automobile is a case for concern in communication studies, particularly around mobilities, privacy, autonomous vehicles, affordances issues of control, freedom, and privacy.

Mobilities are being greatly reshaped in order to respond to contemporary challenges, such as automobile dependence, environmental pollution and so on. Mobilities performed by Autonomous Vehicles (AV), or driverless mobilities, will play a leading role in this process due to the vast array of new possibilities they may bring. In particular, this technology will bring a sharp decrease in the overall cost of mobilities system, including their spatial, social, environmental etc. negative externalities, and notably contribute to their optimization. However, since their implementation is at a rather immature stage, many aspects including, but not limited to their potential effects, technological readiness, legal issues, ethics and societal acceptance are still largely undiscovered. Aim of this project is to elaborate on the future modus operandi of driverless mobilities, as well as to identify which are the supportive driving forces and the challenges in their implementation. Examination of two cases where driverless mobilities have been practiced, in Aalborg, Denmark and Trikala, Greece, pointed out a strong impact of three elements in making driverless mobilities. Safety goes first. Embodiment of AV into existing mobilities practices will be impossible if actual and perceived levels of safety are not high enough. Then come two factors of equal importance, namely proof of usefulness and perception of people towards them.

This Article examines the application of criminal law to autonomous vehicles. The Article begins by providing a background of autonomous vehicles. The Article then examines the general purposes of punishment and traffic laws. The Article next analyzes criminal responsibility for the operator with regard to rules of the road violations, driving under the influence, reckless driving, and vehicular manslaughter. The Article then examines location specific crimes, physical interference with a vehicle, and hacking. This Article argues that the law should be amended because the current application of criminal and traffic laws to autonomous vehicles will make programming the vehicles challenging and enforcing the laws difficult.

Autonomous vehicles will revolutionize society in the near future. Computers, however, are not perfect, and accidents will occur while the vehicle is in autonomous mode. This Article answers the question of who should be liable when an accident is caused in autonomous mode. This Article addresses the liability of autonomous vehicle by examining products liability through the use of four scenarios: the Distracted Driver; the Diminished Capabilities Driver; the Disabled Driver; and the Attentive Driver. Based on those scenarios, this Article suggests that the autonomous technology manufacturer should be liable for accidents caused in autonomous mode because the autonomous vehicle probably caused the accident. Liability should shift back to the " driver " depending on the nature of the driver and the ability of that person to prevent the accident. Thus, this Article argues that an autonomous vehicle manufacturer should be liable for accidents caused in autonomous mode for the Disabled Driver and partially for the Diminished Capabilities Driver and the Distracted Driver. This Article argues the Attentive Driver should be liable for most accidents caused in autonomous vehicles. Currently, products liability does not allocate the financial responsibility of an accident to the party that is responsible for the accident, and this Article suggests that courts and legislatures need to address tort liability for accidents caused in autonomous mode to ensure that the responsible party bears responsibility for accidents.

The internet-of-things (IoT) environment holds different intelligent components networked together and will enable seamless data communication between the connected components. Connected autonomous vehicles or CAVs are major components of the IoT, and the smooth, reliable, and safe operation of CAVs demands a reliable wireless communication system, which can ensure high connectivity, high throughput and low communication latency. The 5G millimeter-wave or mm-wave communication network offers such benefits, which can be the enabler of CAVs, especially for dense congested areas. In this research, we have evaluated the 5G mm-wave and Dedicated Short Range Communication (DSRC) for different CAV applications in a Network Simulator-3 (ns-3). For the CAV applications, we have evaluated the end-to-end latency, packet loss, and data rate (for both the CAV receiver and transmitter) of the 5G mm-wave. We have found that 5G mm-wave can support CAV safety applications by ensuring lower latency compared to the required minimum latency of 200 milliseconds for the forward collision warning application. For mobility applications, we have found that 5G mm-wave can support multiple CAVs with a high data receive rate, which is enough for real-time high definition video streaming for in-vehicle infotainment, with mean packet delay of 13 milliseconds. The findings from this study establish that 5G mm-wave can be the enabler of future CAVs in congested areas. Using the evaluation framework developed in this paper, public agencies can evaluate 5G mm-wave to support CAVs in congested areas, such as heavy pedestrian areas like downtown, commercial zones, under their jurisdiction.

The ethics of autonomous cars and automated driving have been a subject of discussion in research for a number of years. As levels of automation progress, with partially automated driving already becoming standard in new cars from a number of manufacturers, the question of ethical and legal standards becomes virulent. For exam-ple, while automated and autonomous cars, being equipped with appropriate detection sensors, processors, and intelligent mapping material, have a chance of being much safer than human-driven cars in many regards, situations will arise in which accidents cannot be completely avoided. Such situations will have to be dealt with when programming the software of these vehicles. In several instances, internationally, regulations have been passed, based on legal considerations of road safety, mostly. However, to date, there have been few, if any, cases of a broader ethics code for autonomous or automated driving preceding actual regulation and being based on a broadly composed ethics committee of independent experts. In July 2016, the German Federal Minister of Transport and Digital Infrastructure, Alexander Dobrindt, appointed a national ethics committee for automated and connected driving, which began its work in September 2016. In June 2017, this committee presented a code of ethics which was published in German and in English. It consists of 20 ethical guidelines. Having been a member of this committee, I will present the main ethical topics of these guidelines and the discussions that lay behind them.

Digitalization, a dominant megatrend in today's global world, offers numerous intriguing technological possibilities. Out of these novelties, self-driving cars have rapidly come to be a primary focus; the literature categorizes them as a radical innovation due to the possibility that the mass adoption of self-driving cars would not only radically change everyday life for members of industrialized societies, but calls into question the infrastructural, legal, and social ordering of towns and numerous aspects of transportation in the societies that adopt them. Meanwhile, the results of several international surveys with large samples show that public opinion of self-driving cars is ambivalent, indicating parallel signals of enthusiasm and concern. The aim of this paper is to develop key components of a general strategy for addressing the societal challenges associated with self-driving cars as identified in international surveys and relevant literature and using the framework of responsible innovation.

This patent landscape report categorizes and graphically analyzes research trends around the mechanisms, anti-collision system and braking technologies used by a driverless car from various perspectives and highlights the key companies involved.

The European Parliament resolution of 16 February 2017 on Civil Law Rules on Robotics proposed that the strict liability and the risk management approach are alternative legal instruments to achieve the goals set out by this instrument. The evolution of strict liability is parallel with technological change; our question here is whether the elaborated rules are appropriate for managing new problems. For establishing accountability, the question arises: who is to be held liable for damages and based on which form of liability? Setting aside the issues of product liability and setting aside the independent liability of the most sophisticated autonomous robots having 'electronic personality', this essay concentrates on liability questions of the user, and it examines the strict liability rules instituted by the Hungarian Civil Code and their application in practice. According to the results of our previous research, the judicial practice regarding the general clause of liability for dangerous activity (Section 6:535. HCC) is quite fl exible and covers the liability issues of damage caused by artifi cial intelligence. We observed also that the criterion 'dangerous' means less and less risk of damage within normal circumstances, and this statement of fact in practice also successfully competes with other strict liability rules (i.e. product liability for malfunctioning medical devices, liability for dangerous animals, etc.). The capacity of the 'keeper' or 'operator' of the robot and the emerging new types of risks are also touched upon.

In this study, a deep learning-based model has been proposed so that highway vehicles with autonomous driving capability can recognize the lanes they are in and determine their position on the highway while driving. 7000 highway images with different weather conditions were used to create this deep learning model. During the model training, the values of the training parameters were renewed, experiments were made and the error rate of the created model was tried to be minimized. As a result, in this proposed deep learning-based model, lanes on the highway were detected with an error rate of 0.09%.

Autonomous vehicles will encounter situations where an accident is truly unavoidable, requiring the vehicle to decide whom or what to hit. In such situations, the vehicle will make difficult ethical decisions based upon its programming — more specifically, how its crash-optimization algorithm is programmed.

This Article examines crash-optimization algorithms from an ethical and legal standpoint through the lenses of six moral dilemmas. Ethically, the Article focuses specifically on utilitarian and Kantian ethics. Legally, the Article considers the tort and criminal law implications of crash-optimization algorithms. In addition, the Article discusses whether autonomous vehicles should even make ethical decisions. Concluding that they should make ethical decisions, the next consideration is whose — the car owner’s, the car manufacturer’s, or the government’s — ethical beliefs should the car be programmed to follow. Recognizing that no one party could program a fully ethical vehicle, the Article concludes by asserting that the government should provide partial immunity to the car manufacturer to ensure that the vehicles are programmed according to ethics.

Autonomous, or driverless, vehicles can support and promote active travel, such as walking and cycling, when two basic conditions are met: 1.their access to cities is restricted 2.their use is pooled. In the absence of these two conditions, autonomous vehicles could lead to a decline in active travel in cities and an increase in economic, social and environmental costs. Potential costs are rarely mentioned in the rhetoric about autonomous vehicles, much of which is highly optimistic.

In order to achieve smooth autonomous driving in real-life
urban and highway environments, a motion planner must generate trajectories that are locally smooth and responsive (reactive), and at the same time, far-sighted and intelligent (deliberative). Prior approaches achieved both planning qualities for full-speed-range operations at a high computational cost. Moreover, the planning formulations were mostly a trajectory search problem based on a single weighted cost, which became hard to tune and highly scenario-constrained due to overtting. In this paper, a pipelined (phased) framework with tunable planning modules is proposed for general on-road motion planning to reduce the computational overhead and improve the tunability of the planner.

The advancement of technology that makes it possible to entrust tasks traditionally performed by the driver to automated systems increasingly connected with road infrastructures requires a reconsideration of the rules governing road traffic and civil liability in the event of accidents.
The main legal issues generated by the spread of highly automated vehicles and the solutions that are emerging in the context of the European Union and the US will be reconstructed on the basis of an interdisciplinary approach.

at the University of Geneva, 3rd December 2020
invited by Doris Forster

Machine Language is being used in technological advancement in all the domains nowadays, automotive domain being one of them. But practically implementing the ML has its own challenges in the automotive domain since it concerns the lives of people in the car, pedestrians and other vehicles. Even though AI had been in existence for long, the challenges are so high that still we are not in a stage where Level 5 autonomous cars can be deployed in any part of the globe. Here we look into the various challenges faced in the Autonomous cars and different implementations performed.

Self-driving cars are also called autonomous vehicles are one of the predominant forms of artificial intelligence and machine learning which can sense the environment and move safely with no human input for a sophisticated mode of travel. An entire research related study on the autonomous vehicle is done to get in-depth knowledge about every component involved. The automation stages are described with concise knowledge at every level. The automatic lane detection on the roads which is an important form of tracking will be described in detail with techniques used. The actuators used for acceleration, direction, and stop of a vehicle are controlled for the automation of the car.

La città smart è una «città dei flussi» in cui si muovono costantemente merci, conoscenze, persone, dati. Le nuove tecnologie, tramite sistemi di controllo centralizzati e una pervasiva raccolta di informazioni in tempo reale, permettono di dare vita ad una vera e propria data driven intelligence. Uno degli ambiti in cui i sistemi di monitoraggio smart possono aiutare anche le città europee è quello della mobilità. In tanti centri storici, infatti, sono state introdotte zone a traffico limitato e aree pedonali, finalizzate a ridurre l'inquinamento e a rendere più agevole la mobilità dolce (a piedi, in bicicletta, ecc.) nello spazio pubblico, ma nello stesso tempo questo fenomeno ha limitato il diritto costituzionale alla mobilità individuale, facendolo diventare un diritto condizionato, esercitabile solo in specifici momenti e attraverso determinate modalità. L'ultima frontiera della mobilità smart è rappresentata dai veicoli elettrici driverless (senza guidatore) che garantiscono innegabili opportunità di inclusione sociale (si pensi alle possibilità offerte a persone non vedenti) e di flessibilità del servizio, ma lasciano aperte numerose problematiche nel campo del diritto.

Localization and mapping at the same time is one of our problems in this century, specially in autonomous vehicles. The project revolves around implementing a SLAM algorithm on a two-wheeled robot equipped with a camera and sensor. To do this, we use the robot operating system known as ROS to simulate our robot in the real world. Our robot configuration was studied in detail using Gmapping on ROS.From the configuration we study, we believe that the combination of Kinect and sensor will be the best choice for our sensor, which is supported by empirical evidence.

Autonomous, or driverless, vehicles can support and promote active travel, such as walking and cycling, when two basic conditions are met: 1.their access to cities is restricted 2.their use is pooled. In the absence of these two conditions, autonomous vehicles could lead to a decline in active travel in cities and an increase in economic, social and environmental costs. Potential costs are rarely mentioned in the rhetoric about autonomous vehicles, much of which is highly optimistic.

The prospective introduction of autonomous cars into public traffic raises the question of how such systems should behave when an accident is inevitable. Due to concerns with self-interest and liberal legitimacy that have become paramount in the emerging debate, a contractarian framework seems to provide a particularly attractive means of approaching this problem. We examine one such attempt, which derives a harm minimisation rule from the assumptions of rational self-interest and ignorance of one's position in a future accident. We contend, however, that both contractarian approaches and harm minimisation standards are flawed, due to a failure to account for the fundamental difference between those 'involved' and 'uninvolved' in an impending crash. Drawing from classical works on the trolley problem, we show how this notion can be substantiated by reference to either the distinction between negative and positive rights, or to differences in people's claims. ...

This article aims to present itself as an overview of the world of self-driving cars, giving a glimpse albeit fleeting to my research project and clarifying some basic concepts to understand the future developments of this discipline. Concepts like automation levels, self-driving cars, artificial intelligence are essentials and most important to understand all the innovative flow of autonomous driving. Autonomous driving represents the challenge of the future, although many critical aspects must be overcome and addressed.

This paper investigates real-time on-road motion planning algorithms for autonomous passenger vehicles (APV) in urban environments, and propose a computationally efficient planning formulation. Two key properties, tunability and stability, are emphasized when designing the proposed planner. The main contributions of this paper are:
1. A computationally efficient decoupled space-time trajectory
planning structure.
2. The formulation of optimization-free elastic-band-based
path planning and speed-constraint-based temporal planning
routines with pre-determined runtime.
3. Identification of continuity problems with previous cost-based planners that cause tunability and stability issues.

This paper investigates real-time on-road motion planning algorithms for autonomous passenger vehicles (APV) in urban environments, and proposed a computational efficient planning formulation. Two key properties, tunability and stability, are emphasized when designing the proposed planner. The main contributions of this paper are: • A computationally efficient decoupled space-time trajectory planning structure. • The formulation of optimization-free elastic-band-based path planning and speed-constraint-based temporal planning routines in predetermined runtime. • Identification of continuity problems with previous costbased lattice planners that caused tunability and stability issues.

We describe development and user testing of an ambient display for fully autonomous vehicles. Instead of providing feedback about driving actions once executed, it communicates driving decisions in advance, via light signals in passengers' peripheral vision. This was tested in an WoZ-based on-the-road-driving emulation of a autonomous vehicle. Findings from a preliminary study with 14 participants suggest that such a display might be particularly useful to communicate upcoming inertia changes for passengers.

Prior autonomous navigation systems focused on the demonstration of the technological feasibility. But as the technology evolves, improving user experience through learning expert’s or individual’s driving pattern emerges as a promising research direction. As a first step toward this goal, we investigate methods to learn from human demonstrations in urban scenarios without any environmental disturbances (traffic-free). We propose a path model that generates a reference path with smooth and peak-value-reduced curvature, and a parameterized speed model to be fitted by human driving data. Model parameters are then learned through regression methods, and certain statistical human driving patterns are revealed. The learned model is then evaluated by comparing the generated plan with the collected data by the same human driver.

The paper approaches the actual situation of carpooling, by reporting the most updated data divided by its different orms: private, corporate and urban carpooling. For this last kind some innovative experience are reported: instant carpooling, carpooling integrated with car sharing and with other transport modes. The potential of carpooling in the future scenario of disruptive technologies, such as the autonomous driving cars and the dynamic road charging is analyzed. Finally a proposal for some recommendations related to the user-centered approach and on the role of the different stakeholders.

Napjainkra, hosszú kísérletezések után, látható közelségbe kerültünk az önvezető autók megvalósulásához. Hatásuk azonban várhatóan nemcsak annyi, hogy ezentúl még könnyebb lesz a gépjárművek vezetése, hanem az élet szinte minden területén alapvető változásokat hoznak. A cikkben részben a vonatkozó szakirodalom feldolgozása, részben pedig azok továbbgondolása révén, megpróbálunk felvázolni néhány logikus következményt, hogy mi, hogyan és miért változhat meg a gazdaság és társadalom területén közép-és hosszú távon. Kísérletet teszünk annak felvázolására is, hogy Magyarország ezeket a várható tendenciákat hogyan tudja felhasználni a közepes jövedelmi csapdából való kitörésre. Journal of Economic Literature (JEL) kódok: D31, D91, E21, E22, H41, H54, L62

Natural outdoor environments present major navigation challenges to autonomous mobile robots as they involve large amounts of complex sensory data with uncertainties that are inherent to the sensing systems and the unstructured highly dynamic environments involved. The traditional type-1 Fuzzy Logic Controller (FLC) using crisp type-1 fuzzy sets cannot directly model and handle all such uncertainties. A Type-2 Fuzzy Logic Controller (FLC) using type-2 fuzzy sets can model and minimise the effect of uncertainties to produce a better performance. In this paper we present a novel control architecture for autonomous mobile robots based on a hierarchy of Type-2 FLCs to implement the basic navigation behaviours and the coordination between robot control behaviours. We have implemented this architecture in different types of mobile robots navigating in indoor and outdoor unstructured and challenging environments. The type-2 based control system dealt with the linguistic and numerical uncertainties associated with the changing environment and robot conditions and resulted in very good performance that out-performed the type-1 based control system whilst achieving a significant rule reduction compared to the type-1 system.

Throughout the last century, the automobile industry
achieved remarkable milestones in manufacturing reliable,
safe, and affordable vehicles. Because of significant recent
advances in computation and communication technologies,
autonomous cars are becoming a reality. Already autonomous
car prototype models have covered millions of miles in test
driving. Leading technical companies and car manufacturers
have invested a staggering amount of resources in autonomous
car technology, as they prepare for autonomous cars’ full
commercialization in the coming years. However, to achieve this
goal, several technical and non-technical issues remain: software
complexity, real-time data analytics, and testing and verification
are among the greater technical challenges; and consumer stimulation,
insurance management, and ethical/moral concerns rank
high among the non-technical issues. Tackling these challenges
requires thoughtful solutions that satisfy consumers, industry,
and governmental requirements, regulations, and policies. Thus,
here we present a comprehensive review of state-of-the-art results
for autonomous car technology. We discuss current issues that
hinder autonomous cars’ development and deployment on a
large scale. We also highlight autonomous car applications that
will benefit consumers and many other sectors. Finally, to
enable cost-effective, safe, and efficient autonomous cars, we
discuss several challenges that must be addressed (and provide
helpful suggestions for adoption) by designers, implementers,
policymakers, regulatory organizations, and car manufacturers.