  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.
