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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.00460 |
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| _version_ | 1866910508093800448 |
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| author | Frederic, Bouchard Sean, Sedwards Krzysztof, Czarnecki |
| author_facet | Frederic, Bouchard Sean, Sedwards Krzysztof, Czarnecki |
| contents | Autonomous vehicles require highly sophisticated decision-making to determine their motion. This paper describes how such functionality can be achieved with a practical rule engine learned from expert driving decisions. We propose an algorithm to create and maintain a rule-based behaviour planner, using a two-layer rule-based theory. The first layer determines a set of feasible parametrized behaviours, given the perceived state of the environment. From these, a resolution function chooses the most conservative high-level maneuver. The second layer then reconciles the parameters into a single behaviour. To demonstrate the practicality of our approach, we report results of its implementation in a level-3 autonomous vehicle and its field test in an urban environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00460 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Rule-Based Behaviour Planner for Autonomous Driving Frederic, Bouchard Sean, Sedwards Krzysztof, Czarnecki Artificial Intelligence Robotics Autonomous vehicles require highly sophisticated decision-making to determine their motion. This paper describes how such functionality can be achieved with a practical rule engine learned from expert driving decisions. We propose an algorithm to create and maintain a rule-based behaviour planner, using a two-layer rule-based theory. The first layer determines a set of feasible parametrized behaviours, given the perceived state of the environment. From these, a resolution function chooses the most conservative high-level maneuver. The second layer then reconciles the parameters into a single behaviour. To demonstrate the practicality of our approach, we report results of its implementation in a level-3 autonomous vehicle and its field test in an urban environment. |
| title | A Rule-Based Behaviour Planner for Autonomous Driving |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2407.00460 |