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.
@article{arxiv.2407.00460,
title = {A Rule-Based Behaviour Planner for Autonomous Driving},
author = {Bouchard Frederic and Sedwards Sean and Czarnecki Krzysztof},
journal= {arXiv preprint arXiv:2407.00460},
year = {2024}
}
Comments
Use https://link.springer.com/chapter/10.1007/978-3-031-21541-4_17 for citations