Reactive and Safe Road User Simulations using Neural Barrier Certificates

• URL: http://arxiv.org/abs/2109.06689v1
• Date: Tue, 14 Sep 2021 13:45:37 GMT
• Title: Reactive and Safe Road User Simulations using Neural Barrier Certificates
• Authors: Yue Meng, Zengyi Qin, Chuchu Fan
• Abstract summary: We propose a reactive agent model which can ensure safety without comprising the original purposes. Our learned road user simulation models can achieve a significant improvement in safety. Our learned reactive agents are shown to generalize better to unseen traffic conditions.
• Score: 9.961324632236499
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications. In this work, we proposed a reactive agent model which can ensure safety without comprising the original purposes, by learning only high-level decisions from expert data and a low-level decentralized controller guided by the jointly learned decentralized barrier certificates. Empirical results show that our learned road user simulation models can achieve a significant improvement in safety comparing to state-of-the-art imitation learning and pure control-based methods, while being similar to human agents by having smaller errors to the expert data. Moreover, our learned reactive agents are shown to generalize better to unseen traffic conditions, and react better to other road users and therefore can help understand challenging planning problems pragmatically.

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