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Self-driving technology may lead to a paradigm shift for the transport industry with shared cars available to everyone. However, this vision has increasingly been challenged as too optimistic and unsubstantiated. In this study we explore societal impacts of using this technology for both cars and public transport and investigate differences depending on geography and trip purpose. Four scenarios were designed through workshops with 130 transport experts, modelled using a conventional four-step model for Stockholm, Sweden and evaluated in terms of changes to mode choice, number of trips and person kilometres. We find larger increases for non-commuting trips, i.e. service and leisure trips, than for commuting trips, questioning the view of the ‘productive work trip’ as self-driving technology’s main impact on society. As these trips are primarily made outside of rush hours, this may lead to a changed transport system. Geographic differences are substantial and heavily dependent on the cost model for car alternatives, even indicating a reduction in car travel in rural areas if private ownership would be replaced by shared cars. Furthermore, walking and cycling levels decreased in all scenarios while enhancing public transport using self-driving technology has a limited impact on ridership. These results show that the impacts of self-driving technology may have varied societal impacts even within a region and may lead to increased car travel, especially off-peak. These conclusions stress the need for policies that are sensitive to both geography and time., QC 20220518

Numerous studies have studied the impacts of self-driving technology on e.g. accident rates or CO2 emissions using various frameworks. In this paper we present an overview of previous frameworks used for societal impacts, and review their advantages and limitations. Additionally, we introduce the Total Impact Assessment (TIA) framework developed by the Swedish Transport Administration and use this framework to evaluate three scenarios for self-driving bus services in Stockholm. We conclude that the reviewed frameworks cover different aspects of self-driving technology, and that e.g. cybersecurity and biodiversity are areas neglected by most frameworks. Furthermore, most frameworks assume effects to be homogenous, when there may be large variation in e.g. perceived security. The TIA framework does not manage to include all societal aspects of self-driving technology, but has great benefits and manages to provide important insights of the societal impacts of self-driving technology, especially how effects may wary for different actors., QC 20220518

The introduction of more automation into our vehicles is increasing our ability to avoid or mitigate the effects of collisions. Early systems could brake when a likely collision was detected, while more advanced systems will be able to steer to avoid or reconfigure a collision during the same circumstances. This paper addresses how the post-impact motion of an impacted vehicle could be included in the decision making process of severity minimization motion planning. A framework is proposed that builds on previous work by the authors, combining models from motion planning, vehicle dynamics and accident reconstruction. This framework can be configured for different contexts by adjusting its cost function according to relevant risks. Simulations of the unified system are carried out and analysed from the perspective of vehicle model complexity and collision parameters sensitivity. Additionally, effects are highlighted concerning different modeling decisions, with respect to vehicle dynamics models and collision models, that are important to consider in further research., QC 20220530

The introduction of more automation into our vehicles is increasing our ability to avoid or mitigate the effects of collisions. Early systems could brake when a likely collision was detected, while more advanced systems will be able to steer to avoid or reconfigure a collision during the same circumstances. This paper addresses how the post-impact motion of an impacted vehicle could be included in the decision making process of severity minimization motion planning. A framework is proposed that builds on previous work by the authors, combining models from motion planning, vehicle dynamics and accident reconstruction. This framework can be configured for different contexts by adjusting its cost function according to relevant risks. Simulations of the unified system are carried out and analysed from the perspective of vehicle model complexity and collision parameters sensitivity. Additionally, effects are highlighted concerning different modeling decisions, with respect to vehicle dynamics models and collision models, that are important to consider in further research., QC 20220530

The introduction of more automation into our vehicles is increasing our ability to avoid or mitigate the effects of collisions. Early systems could brake when a likely collision was detected, while more advanced systems will be able to steer to avoid or reconfigure a collision during the same circumstances. This paper addresses how the post-impact motion of an impacted vehicle could be included in the decision making process of severity minimization motion planning. A framework is proposed that builds on previous work by the authors, combining models from motion planning, vehicle dynamics and accident reconstruction. This framework can be configured for different contexts by adjusting its cost function according to relevant risks. Simulations of the unified system are carried out and analysed from the perspective of vehicle model complexity and collision parameters sensitivity. Additionally, effects are highlighted concerning different modeling decisions, with respect to vehicle dynamics models and collision models, that are important to consider in further research., QC 20220530