The World of Driverless Cars and Emerging Artificial Intelligence
Gates, B. (2015, February). The next outbreak? We’re not ready [Video]. TED Conferences. https://www.ted.com/talks/bill_gates_the_next_outbreak_we_re_not_ready
Plan : I will integrate insights from a TED Talk interview into my research proposal to provide expert perspectives on the role of artificial intelligence (AI) in the development and implementation of driverless cars. This source will be used in the following sections: Incorporate expert analysis from the TED Talk on how AI enables decision-making in self-driving cars
summary : The TED Talk provides expert insights into the transformative role of artificial intelligence (AI) in the development of driverless cars. The speaker discusses how AI-powered automation is revolutionizing transportation by improving traffic efficiency, reducing accidents, and enhancing mobility. However, the talk also addresses key concerns such as data privacy, cybersecurity threats, and the ethical implications of AI decision-making. The speaker emphasizes the need for robust safety regulations, trust-based security mechanisms, and ethical AI development to ensure a responsible transition to autonomous vehicles. The talk concludes with predictions on how AI-driven transportation will shape the future of mobility and urban infrastructure.
BBC News. (2023, October 5). Elon Musk talks about AI and the future of Tesla [Video]. YouTube.
plan : I plan to use the cited YouTube interview in my research proposal to provide expert insights and real-world perspectives on the impact of driverless cars and AI in transportation. The video will support various sections of my research as Use expert opinions from the video to establish credibility and emphasize the importance of AI in driverless technology.
summary :Summarize key findings by integrating expert predictions from the interview about the long-term impact of AI in transportation.
Ghosh, I. (2021, November 26). How driverless cars will change our world. BBC Future
Plan: Use the second quote to examine environmental advantages, citing research on decreased carbon emissions resulting from optimized driving patterns and reduced congestion
Quotes:
Privacy and security concerns remain significant hurdles in the integration of driverless technology into everyday life
The widespread adoption of autonomous vehicles could lead to a substantial decrease in carbon emissions
Self-driving cars have the potential to significantly reduce traffic accidents caused by human error
Summary : How driverless cars will change our world" explores the transformative impact of autonomous vehicles on various aspects of society. It discusses potential benefits such as reduced traffic congestion, decreased accidents, and lower carbon emissions. The article also addresses challenges, including privacy concerns and the need for new security measures in the era of driverless technology
Szalay, Z., Tettamanti, T., Esztergár-Kiss, D., Varga, I., & Bartolini, C. (2018). Development of a test track for driverless cars: Vehicle design, track configuration, and liability considerations. Periodica Polytechnica. Transportation Engineering, 46(1), 29. https://doi.org/10.3311/PPtr.10753
plan: In my research proposal on "The World of Driverless Cars and Emerging Artificial Intelligence," I plan to incorporate information about The Research Center for Autonomous Road Vehicles (RECAR) to support discussions on the role of academic and industrial collaboration in advancing autonomous vehicle technology.
Quotes
In a driverless car, the car occupant cannot be considered a “driver”, and therefore the normal rules for driver’s licenses and liability are jeopardized. pg 34
As communication protocol, the UDP (User Datagram Protocol) is suggested between traffic control system and PLC components. pg 33
The trajectory execution layer receives the selected trajectory as an input from the planning layer. After the trajectory segmentation and the motion vector generation (containing longitudinal and lateral control commands), the execution of the motion vector is performed by distributing it among the intelligent actuators of the vehicle drivetrain. pg 31
summary : The Research Center for Autonomous Road Vehicles (RECAR) was founded in 2015 upon the initiative of the Faculty of Transportation Engineering and Vehicle Engineering of Budapest
University of Technology and Economics. The research center is supported by industrial partners and other academic partners targeting research and educational purposes
Karmakar, G., Chowdhury, A., Das, R., Kamruzzaman, J., & Islam, S. (2021). Assessing trust level of a driverless car using deep learning. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4457-4466. https://doi.org/10.1109/TITS.2021.3059261
plan: i plan to use this To understand how automation is transforming transportation infrastructure.
Quote
The impact created by attacks in camera is substantially different from that produced by LiDAR and radar.
There exists many deep learning algorithms. In this paper, we use Deep Neural networks (DNNs) in our proposed trustworthiness measure.
LiDAR is used to measure the distances between the vehicle and the surrounding objects. It can send 400,000 pulses in a second and is used to create 3D maps of the surroundings
of an autonomous car for better and safe driving performance as it can operate in severe conditions like low visibility, fog, and even in complete darkness. LiDAR uses infrared light pulses to detect the objects’ shapes and distances that exist surrounding the car
Summary:The increasing adoption of driverless cars already providing a shift to move away from traditional transportation systems to automated ones in many industrial and commercial applications. Recent research has justified that driverless vehicles will considerably reduce traffic congestions, accidents, carbon emissions, and enhance the accessibility of driving to wider cross-section of people and lifestyle choices. However, at present, people’s main concerns are about its privacy and security. Since traditional protocol layers based security mechanisms are not so effective for a distributed system, trust value-based security mechanisms, a type of pervasive security, are appearing as popular and promising techniques
Khanderia, S. (2023). Driverless cars and the determination of the manufacturer's liability for injuries: Is india ready? Information & Communications Technology Law, 32(3), 356-384. https://doi.org/10.1080/13600834.2023.2231324
Plan:Use this paper to illustrate how different countries are preparing for driverless technology and how India, with its focus on shared mobility, may become a major market for autonomous vehicles.
Qoutes:
In the absence of choice, the liability
would be ascertained according to the law of the habitual residence of the person
suffering the damage and the manufacturer – provided it is common. Pg 10
The freedom is limited to the choice of a legal system. Therefore, parties may not choose the non-
binding legal principles such as soft law to govern their dispute. Pg 10
In principle, the measure aims to redress consumers from the challenges that arise
from thriving competition and technological advances, which while on the one hand,
increase the available choices but additionally pose a threat to the safety of those
when the product in question turns out to be defective. Pg 8
Summary: India’s endeavour of becoming a leader in shared mobility indicates that it could soon follow suit in becoming one of the leading
driverless car markets across the globe. The paper critically analyses the state of India’s current liability laws on automobile- related injuries in a comparative context. It examines whether
their extension to technology-based automobiles such as driverless cars may be feasible – particularly considering that accidents involving their operation are likely to be attributable to malfunctions in the software or hardware of these vehicles.
Lyons, G. (2022). The driverless cars emulsion: Using participatory foresight and constructive conflict to address transport's wicked problems. Futures : The Journal of Policy, Planning and Futures Studies, 136https://doi.org/10.1016/j.futures.2021.102889
Subtopic
Plan: Use this paper to introduce the complexity of the driverless car debate, highlighting how various stakeholders (government, industry, consumers, ethicists) hold conflicting views on its impact.
Quotes
The core concept of the workshop methodology was to ensure that each participant was encouraged to practice critical thinking – challenging unconscious bias through being prepared to, and having the opportunity to, listen carefully to contrary opinions from others, helped by engagement with both utopian and dystopian thinking tasks. Pg 11
Holding a variety of roles at different levels of seniority (from Chief Executive to student), most but not all participants indicated that they were, in effect, pragmatists - involved in addressing the prospects of a driverless cars future in their paid work. Pg 9
A fourth intersecting constituency was also referred to: pragmatists (those who devote their expertise to the R&D drive to make driverless cars happen because the work is interesting and it ‘pays the bills’). A pragmatist may belong to any one of the three constituencies above. Pg 6
Summary: This paper introduces a novel methodology to the transport sector to foster dialogue between actors holding different perspectives on issues pertinent to the future of mobility that might be termed ‘wicked’. The case of driverless cars is considered. The paper points to a dearth of interaction between actors holding different and sometimes polar opposite views on what driverless cars could mean for the future of transport and society. It examines the role of bringing diverse perspectives together in a collaborative setting to address this wicked problem. The importance of creating task conflict is highlighted in the facilitation of engagement and achievement of shared learning.
Academic source 2 : Broggi, A., Cerri, P., Debattisti, S., Laghi, M. C., Medici, P., Molinari, D., Panciroli, M., & Prioletti, A. (2015). PROUD-public road urban driverless-car test. IEEE Transactions on Intelligent Transportation Systems, 16(6), 3508-3519. https://doi.org/10.1109/TITS.2015.2477556
Plan: Use this paper to highlight the challenges in fostering productive discussions among stakeholders in the driverless car industry, such as policymakers, engineers, businesses, and the public. It provides a framework for understanding the lack of consensus on the future implications of autonomous vehicles.
Quote
The track was kept confidential, and vehicles had to work in an environment with high uncertainty depending on their on-board devices and self-driving system.
Exploiting this map, a speed profile is computed in order to avoid or mitigate collision with dynamic obstacles, be consistent with road speed limits, and to achieve a smooth and comfortable driving experience.
The high level control creates a path composed of a set of roads and junctions, connecting the current vehicle position to the destination specified by the user. Maps coming from the “Open Street Map Project” [29] have been used as a cartographic source, thus fulfilling the aforementioned constraint of openness of the used data. Page 6
Summary: This paper presents an autonomous driving test held in Parma on urban roads and freeways open to regular traffic. During this test, the vehicle not only performed simple maneuvers, but it had to cope with complex driving scenarios as well, including roundabouts, junctions, pedestrian crossings, freeway junctions, and traffic lights. The test demonstrated the ability of the current technology to manage real situations and not only the well-structured and predictable ones. A comparison of milestones, challenges, and key results in autonomous driving is presented
to highlight the novelty and the specific purpose of the test.
Academic source 1: Kaur, K., & Rampersad, G. (2018). Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars. Journal of Engineering and Technology Management, 48, 87-96. https://doi.org/10.1016/j.jengtecman.2018.04.006
Plan: I will use this source to support my arguments about the transformative potential of AI in the transportation sector.
Quote / paraphrase
Machine learning algorithms are essential for training autonomous vehicles to adapt to different driving conditions and improve their performance over time. pg 91
Trust can be defined as one’s willingness to place himself/ herself in a vulnerable position, with respect to a technology, with a positive expectation of an outcome or a positive nature of future behavior. Pg 90
With autonomous vehicles not far away, their reliability is only constrained by the technology and computing power that they carry. Pg 86
Summary: Driverless cars are seen as one of the key disruptors in the next technology revolution. However, the main barrier to adoption is the lack of public trust. The purpose of this study is to investigate the key factors influencing the adoption of driverless cars. Drawing on quantitative evidence, the study found that the ability of the driverless car to meet performance expectations and its reliability were important adoption determinants. Significant concerns included privacy (autonomy, location tracking and surveillance) and security (from hackers). The paper provides implications for firms developing the next generation of car features and early implementation sites.
