Summary of A Rule-based Behaviour Planner For Autonomous Driving, by Bouchard Frederic et al.
A Rule-Based Behaviour Planner for Autonomous Driving
by Bouchard Frederic, Sedwards Sean, Czarnecki Krzysztof
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel algorithm is proposed for creating and maintaining a rule-based behavior planner for autonomous vehicles. This system learns from expert driving decisions to make sophisticated decisions about motion. A two-layer rule-based theory is used, with the first layer determining feasible behaviors based on environmental state and the second layer reconciling parameters into a single behavior. The approach is demonstrated in a level-3 autonomous vehicle through a field test in an urban environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous cars need to make smart decisions about how to move around. This paper shows how to create a system that can do this by learning from expert drivers. It’s like a set of rules for driving, but instead of being fixed, it adapts to the situation. The system is tested in a self-driving car and shown to work well in an urban environment. |
