2,912 research outputs found
Game Theoretic Analysis of Road User Safety Scenarios Involving Autonomous Vehicles
Interactions between pedestrians, bikers, and human-driven vehicles have been
a major concern in 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 which will be programmed to avoid accidents.
This paper proposes some game theoretical models applied to related traffic
scenarios. In the first two games the reciprocal influence between a pedestrian
and a vehicle (either autonomous or not) is analyzed, while the third game
investigates the intersection of two vehicles, possibly autonomous. The games
have been simulated in order to demonstrate the theoretical analysis and the
predicted behaviors. These investigations can shed new lights on how novel
urban traffic regulations could be required to allow for a better interaction
of vehicles and a general improved management of traffic and communication
vehicular networks.Comment: Accepted at 'IEEE International Symposium on Personal, Indoor and
Mobile Radio Communications' 9-12 September 2018 - Bologna, Italy. Special
Session on 'Wireless Technologies for Connected and Autonomous Vehicles'. 7
pages, 5 figure
Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach
Autonomous vehicles (AVs) are inevitably entering our lives with potential benefits for improved traffic safety, mobility, and accessibility. However, AVs’ benefits also introduce a serious potential challenge, in the form of complex interactions with human-driven vehicles (HVs). The emergence of AVs introduces uncertainty in the behavior of human actors and in the impact of the AV manufacturer on autonomous driving design. This paper thus aims to investigate how AVs affect road safety and to design socially optimal liability rules in comparative negligence for AVs and human drivers. A unified game is developed, including a Nash game between human drivers, a Stackelberg game between the AV manufacturer and HVs, and a Stackelberg game between the law maker and other users. We also establish the existence and uniqueness of the equilibrium of the game. The game is then simulated with numerical examples to investigate the emergence of human drivers’ moral hazard, the AV manufacturer’s role in traffic safety, and the law maker’s role in liability design. Our findings demonstrate that human drivers could develop moral hazard if they perceive their road environment has become safer and an optimal liability rule design is crucial to improve social welfare with advanced transportation technologies. More generally, the game-theoretic model developed in this paper provides an analytical tool to assist policy-makers in AV policymaking and hopefully mitigate uncertainty in the existing regulation landscape about AV technologies
Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior
Abstract—Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians’ likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control
Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles
Interaction-aware Autonomous Driving (IAAD) is a rapidly growing field of
research that focuses on the development of autonomous vehicles (AVs) that are
capable of interacting safely and efficiently with human road users. This is a
challenging task, as it requires the autonomous vehicle to be able to
understand and predict the behaviour of human road users. In this literature
review, the current state of IAAD research is surveyed in this work. Commencing
with an examination of terminology, attention is drawn to challenges and
existing models employed for modelling the behaviour of drivers and
pedestrians. Next, a comprehensive review is conducted on various techniques
proposed for interaction modelling, encompassing cognitive methods, machine
learning approaches, and game-theoretic methods. The conclusion is reached
through a discussion of potential advantages and risks associated with IAAD,
along with the illumination of pivotal research inquiries necessitating future
exploration
Sampling-Based Threat Assessment Algorithms for Intersection Collisions Involving Errant Drivers
This paper considers the decision-making problem for a vehicle crossing a road
intersection in the presence of other, potentially errant, drivers. This problem is considered in
a game-theoretic framework, where the errant drivers are assumed to be capable of causing
intentional collisions. Our approach is to simulate the possible behaviors of errant drivers using
RRT-Reach, a modi ed application of rapidly-exploring random trees. A novelty in RRT-Reach
is the use of a dual exploration-pursuit mode, which allows for e cient approximation of the
errant reachability set for some xed time horizon. Through simulation and experimental results
with a small autonomous vehicle, we demonstrate that this threat assessment algorithm can be
used in real-time to minimize the risk of collision
Defining interactions: a conceptual framework for understanding interactive behaviour in human and automated road traffic
Rapid advances in technology for highly automated vehicles (HAVs) have raised concerns about coexistence of HAVs and human road users. Although there is a long tradition of research into human road user interactions, there is a lack of shared models and terminology to support cross-disciplinary research and development towards safe and acceptable interaction-capable HAVs. Here, we review the main themes and findings in previous theoretical and empirical interaction research, and find large variability in perspectives and terminologies. We unify these perspectives in a structured, cross-theoretical conceptual framework, describing what road traffic interactions are, how they arise, and how they get resolved. Two key contributions are: (1) a stringent definition of “interaction”, as “a situation where the behaviour of at least two road users can be interpreted as being influenced by the possibility that they are both intending to occupy the same region of space at the same time in the near future”, and (2) a taxonomy of the types of behaviours that road users exhibit in interactions. We hope that this conceptual framework will be useful in the development of improved empirical methodology, theoretical models, and technical requirements on vehicle automation
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