Safer Autonomous Driving in a Stochastic, Partially-Observable Environment by Hierarchical Contingency Planning

• URL: http://arxiv.org/abs/2204.06509v1
• Date: Wed, 13 Apr 2022 16:47:00 GMT
• Title: Safer Autonomous Driving in a Stochastic, Partially-Observable Environment by Hierarchical Contingency Planning
• Authors: Ugo Lecerf, Christelle Yemdji-Tchassi, Pietro Michiardi
• Abstract summary: An intelligent agent should be prepared to anticipate a change in its belief of the environment state. This is especially the case for autonomous vehicles (AVs) navigating real-world situations where safety is paramount. We show that our approach results in robust, safe behaviour in a partially observable, safe environment, generalizing well over environment not seen during training.
• Score: 10.971411555103574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When learning to act in a stochastic, partially observable environment, an intelligent agent should be prepared to anticipate a change in its belief of the environment state, and be capable of adapting its actions on-the-fly to changing conditions. As humans, we are able to form contingency plans when learning a task with the explicit aim of being able to correct errors in the initial control, and hence prove useful if ever there is a sudden change in our perception of the environment which requires immediate corrective action. This is especially the case for autonomous vehicles (AVs) navigating real-world situations where safety is paramount, and a strong ability to react to a changing belief about the environment is truly needed. In this paper we explore an end-to-end approach, from training to execution, for learning robust contingency plans and combining them with a hierarchical planner to obtain a robust agent policy in an autonomous navigation task where other vehicles' behaviours are unknown, and the agent's belief about these behaviours is subject to sudden, last-second change. We show that our approach results in robust, safe behaviour in a partially observable, stochastic environment, generalizing well over environment dynamics not seen during training.

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