Learning Pedestrian Social Behaviour for Game-Theoretic Self-Driving Cars

Camara, Fanta and Fox, Charles (2022) Learning Pedestrian Social Behaviour for Game-Theoretic Self-Driving Cars. In: RSS Pioneers Workshop, 26 June 2022, New York.

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Learning Pedestrian Social Behaviour for Game-Theoretic Self-Driving Cars
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Abstract

Robot navigation in environments with static objects appears to be a solved problem, but navigating around humans in dynamic and unstructured environments remains an active research question. This requires not only advanced path planning methods but also a good perception system, models of multi-agent interactions and realistic hardware for testing. To evolve in human social spaces, robots must also show social intelligence, i.e. the ability to understand human behaviour via explicit and implicit communication cues (e.g. proxemics) for better human-robot interactions (HRI) [28]. Similarly, autonomous vehicles (AVs), also called “self-driving cars” that are appearing on the roads need a better understanding of pedestrians’ social behaviour, especially in urban areas [26]. In particular, previous work showed that pedestrians may take advantage over autonomous vehicles [13] by intentionally and constantly stepping in front of AVs, hence preventing them from making progress on the roads. This inability of current AVs to read the intention of other road users, predict their future behaviour and interact with them is known as “the big problem with self-driving cars” [1]. Thus, AVs need better decision-making models and must find a good balance between stopping for pedestrians when required and driving to reach their final destination as quickly as possible for their on-board passengers. A comprehensive review of existing pedestrian models for AVs, ranging from low-level sensing, detection and tracking models [9] to high-level interaction and game theoretic models of pedestrian behaviour [10], found that the lower-level models are accurate and mature enough to be deployed on AVs but more research is needed in the higher-level models. Hence, in this work, we focus on modelling, learning and operating pedestrian high-level social behaviour on self-driving cars using game theory and proxemics.

Keywords:robotics, autonomous vehicles
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:50876
Deposited On:14 Sep 2022 13:58

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