Game Theoretic Analysis of Road User Safety Scenarios Involving Autonomous Vehicles

By Umberto Michieli, Leonardo Badia. 2018

37 citations recorded by the source corpus.

Interactions between pedestrians, cyclists, and human-driven vehicles have become a major concern for traffic safety over the years. The upcoming age of autonomous vehicles will further raise major problems on whether self-driving cars can accurately avoid accidents; on the other hand, usability issues arise on whether human-driven cars and pedestrians can dominate the road at the expense of the autonomous vehicles that will be programmed to avoid accidents. This paper proposes some game theoretical models applied to traffic scenarios, where the strategic interaction between a pedestrian and an autonomous vehicle is analyzed. The games have been simulated to demonstrate the theoretical analysis and the predicted behaviors. These investigations can shed new lights on how urban traffic regulations and inter-vehicle communications could be required to allow for a general improved management of traffic in the presence of autonomous vehicles.

Subfields: Automotive Engineering, Control and Systems Engineering

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Citations in the corpus

Knowledge Distillation for Incremental Learning in Semantic Segmentation (2019, background): “ever been extensively studied. Nevertheless, semantic segmentation is a key task that artiﬁcial intelligence systems must face frequently in various applications, e.g., autonomous driving or robotics [10, 11, 12]. Dierently from image classiﬁcation, in semantic segmentation each image contains toget”
Incremental Learning Techniques for Semantic Segmentation (2019, neutral): “, in robotics or autonomous driving [3, 22].”
Unsupervised Domain Adaptation for Semantic Segmentation of Urban Scenes (2019, background): “This could potentially help shaping how autonomous vehicles face road scenarios [23].”
Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation (2019, background): “All the approaches for generic images can be applied also to road scenes, however since this is a very relevant application [11], [12] there has been a large effort both in the acquisition of datasets [13]– [15] and in the development of ad-hoc approaches [16]–[18].”