Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866913262863384576 |
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| author | Maci, Enrik Howard, Rhys Kunze, Lars |
| author_facet | Maci, Enrik Howard, Rhys Kunze, Lars |
| contents | Validating the safety of Autonomous Vehicles (AVs) operating in open-ended, dynamic environments is challenging as vehicles will eventually encounter safety-critical situations for which there is not representative training data. By increasing the coverage of different road and traffic conditions and by including corner cases in simulation-based scenario testing, the safety of AVs can be improved. However, the creation of corner case scenarios including multiple agents is non-trivial. Our approach allows engineers to generate novel, realistic corner cases based on historic traffic data and to explain why situations were safety-critical. In this paper, we introduce Probabilistic Lane Graphs (PLGs) to describe a finite set of lane positions and directions in which vehicles might travel. The structure of PLGs is learnt directly from spatio-temporal traffic data. The graph model represents the actions of the drivers in response to a given state in the form of a probabilistic policy. We use reinforcement learning techniques to modify this policy and to generate realistic and explainable corner case scenarios which can be used for assessing the safety of AVs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_13658 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs Maci, Enrik Howard, Rhys Kunze, Lars Artificial Intelligence Robotics E.1; G.1.1; G.3; I.2.6; I.2.9; I.6.0 Validating the safety of Autonomous Vehicles (AVs) operating in open-ended, dynamic environments is challenging as vehicles will eventually encounter safety-critical situations for which there is not representative training data. By increasing the coverage of different road and traffic conditions and by including corner cases in simulation-based scenario testing, the safety of AVs can be improved. However, the creation of corner case scenarios including multiple agents is non-trivial. Our approach allows engineers to generate novel, realistic corner cases based on historic traffic data and to explain why situations were safety-critical. In this paper, we introduce Probabilistic Lane Graphs (PLGs) to describe a finite set of lane positions and directions in which vehicles might travel. The structure of PLGs is learnt directly from spatio-temporal traffic data. The graph model represents the actions of the drivers in response to a given state in the form of a probabilistic policy. We use reinforcement learning techniques to modify this policy and to generate realistic and explainable corner case scenarios which can be used for assessing the safety of AVs. |
| title | Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs |
| topic | Artificial Intelligence Robotics E.1; G.1.1; G.3; I.2.6; I.2.9; I.6.0 |
| url | https://arxiv.org/abs/2308.13658 |