Ethical and Social Aspects of Self-Driving Cars

As an envisaged future of transportation, self-driving cars are being discussed from various perspectives, including social, economical, engineering, computer science, design, and ethics. On the one hand, self-driving cars present new engineering problems that are being gradually successfully solved. On the other hand, social and ethical problems are typically being presented in the form of an idealized unsolvable decision-making problem, the so-called trolley problem, which is grossly misleading. We argue that an applied engineering ethical approach for the development of new technology is what is needed; the approach should be applied, meaning that it should focus on the analysis of complex real-world engineering problems. Software plays a crucial role for the control of self-driving cars; therefore, software engineering solutions should seriously handle ethical and social considerations. In this paper we take a closer look at the regulative instruments, standards, design, and implementations of components, systems, and services and we present practical social and ethical challenges that have to be met, as well as novel expectations for software engineering.Comment: 11 pages, 3 figures, 2 table

Dawn of autonomous vehicles: review and challenges ahead

This paper reviews the state of the art on autonomous vehicles as of 2017, including their impact at socio-economic, energy, safety, congestion and land-use levels. This impact study focuses on the issues that are common denominators and are bound to arise independently of regional factors, such as (but not restricted to) change to vehicle ownership patterns and driver behaviour, opportunities for energy and emissions savings, potential for accident reduction and lower insurance costs, and requalification of urban areas previously assigned to parking. The challenges that lie ahead for carmakers, law and policy makers are also explored, with an emphasis on how these challenges affect the urban infrastructure and issues they create for municipal planners and decision makers. The paper concludes with strengths, weaknesses, opportunities, and threats analysis that integrates and relates all these aspects.info:eu-repo/semantics/publishedVersio

Improving Automated Driving through Planning with Human Internal States

This work examines the hypothesis that partially observable Markov decision process (POMDP) planning with human driver internal states can significantly improve both safety and efficiency in autonomous freeway driving. We evaluate this hypothesis in a simulated scenario where an autonomous car must safely perform three lane changes in rapid succession. Approximate POMDP solutions are obtained through the partially observable Monte Carlo planning with observation widening (POMCPOW) algorithm. This approach outperforms over-confident and conservative MDP baselines and matches or outperforms QMDP. Relative to the MDP baselines, POMCPOW typically cuts the rate of unsafe situations in half or increases the success rate by 50%.Comment: Preprint before submission to IEEE Transactions on Intelligent Transportation Systems. arXiv admin note: text overlap with arXiv:1702.0085