A Rule-Based Behaviour Planner for Autonomous Driving
This work addresses the need for reliable decision-making in autonomous driving, but it appears incremental as it builds on existing rule-based methods.
The paper tackled the problem of decision-making for autonomous vehicles by proposing a rule-based behavior planner learned from expert driving decisions, and demonstrated its practicality through implementation and field testing in an urban environment.
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.