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This paper is dedicated to the topic of the emerging challenges of traditional criminal law as posed by the development of modern technology. In certain parts of the world, the automotive industry has already implemented a new generation of autonomous self-driving vehicles. Moreover, there have been incidents where such vehicles have been involved in traffic accidents with deadly consequences. The use of autonomous intelligence is also emerging in other important sectors, such as in medicine and the military.

The issue of the legal liability of autonomous machines has been the subject of numerous philosophical debates and approached from the perspective of tort law. The question of criminal liability, however, has still not been debated more comprehensively. In this text, I will analyze the scope and limits of criminal liability of humans for criminal offenses “committed” by autonomous systems. Firstly, I will describe potential crimes of AI in context of intent and negligence. Secondly, I will propose the new concept of (shared) criminal liability, the concept I will name the Division of Labor theory.

This chapter explores how technology availability and costs influence public opinion, vehicle ownership decisions, travel, and location choices. Attitudes towards electric vehicles (EVs) are considered within the broader context of other linked technological trends affecting automobility, with a particular focus on the shift to (electric powertrain) autonomous vehicles (AVs).

This chapter draws upon modelling of quantitative survey data from 1,426 Americans, which employed regression analysis to predict and understand variables linked to the preference for an AV over a human driver, percentage of trips taken by an AV, percentage of trips using dynamic ride-sharing (DRS) inside a shared autonomous vehicle (SAV), and factors affecting EV charging access in home and at work/school.

The findings show that full EV charging times significantly affect decisions for next household vehicle purchase. A lack of charging ability at home appears to be a significantly greater hindrance to respondents’ willingness to purchase full EVs than does a lack of charging ability at work. And home location choice impacts of AVs are not expected to be substantial. Considering future EV/AV ride-sharing (an important component of sustainable future mobility systems), DRS may ease congestion if SAV riders widely adopt DRS for work and school trips; however, sharing with strangers may not be popular in practice.

This chapter is useful to manufacturers and fleet operators for pricing and marketing decisions, and public transit authorities/providers can benefit from understanding evolving travel choices and land use patterns to craft equitable policies, and model future transportation demand to help plan services and infrastructure projects.

Automated vehicles are likely to have significant impacts on the transport system such as increased road capacity, more productive/enjoyable time spent travelling in a car, and increased vehicle kilometres travelled. However, there is a great risk that automated driving may negatively impact the environment if adequate policies are not put in place. This chapter examines the effects of driverless vehicles and the types of policies required to attain sustainable implementation of the technology. To understand the effects on a systemic level, and to understand the needs and impacts of policies, the dynamics must be understood. Therefore, a causal loop diagram (CLD) is developed and analysed. One important insight is that the effects of driverless vehicles are mainly on the vehicular level (e.g., the reduced number of accidents per vehicle). These effects can be cancelled out on a systemic level (e.g., due to increased vehicle-kilometre travelled (VKT) that increases total number of accidents). The marginal costs of road transport are central to both freight and passenger transport. Automation will reduce marginal costs and shift the equilibrium in the transport system towards a state with higher VKT. This will lead to greater energy consumption and higher emissions. To attain sustainability goals, there might be a need to balance this reduction of marginal costs by using policy instruments. In the work, CLDs is experienced to be a useful tool to support the collaboration between experts from different fields in the dialogue about policies.

Automated vehicle technologies dominate many visions of future systems of smart mobility. This chapter uses the approach of Transition Management to explore the multi-actor governance processes around automated vehicle technologies in the United Kingdom (UK), with specific attention being paid to the role of the UK government. It shows the relatively comprehensive approach to automated vehicle innovation that has been adopted by the UK government, emerging across multiple domains including the creation of positive discourses around automation, and the facilitation of network building and demonstration projects. Framed by the Transition Management cycle of strategic, tactical, operational and reflexive activities, the chapter argues for greater integration across the levels of the cycle, and experimentation that moves beyond technological capability, to include the heterogeneous publics, in a more diverse set of roles than the current framing of ‘potential technology adopter’. The chapter points to the techno-optimism displayed by governments participating in the international race to vehicle automation, often with dual roles as both producers and consumers, and suggest that greater inclusivity, democracy, diversity and openness in the innovation process may contribute to context sensitive implementation.