Achieving Weight Coverage for an Autonomous Driving System with Search-based Test Generation

Autonomous Driving Systems (ADS) are complex critical systems that need to be thoroughly tested. Still, assessing the strength of tests for such systems is an open and complex problem. A central component of an ADS is the Path Planner, which is in charge of computing the trajectory of the autonomous vehicle. It bases its decisions on several aspects such as safety, traffic regulations, comfort, etc. These aspects can be linked to weights in a weighted cost function that ranks potential trajectories to be followed. Weight coverage has been proposed as a test criterion for tests of this type of path planner. Weight coverage measures how much the different weights (and thus the aspects they are linked to) are involved in the decisions taken by the path planner in a test scenario. All weights should be involved in at least one test. Although weight coverage has shown to be a reasonable criterion, it does not provide a clear way to drive the generation of new scenarios. In this paper, we propose a search-based approach for generating scenarios for achieving weight coverage. We introduce two variants of the approach; the first one tries to generate a scenario covering a given single weight, while the second one tries to generate scenarios covering as many weights as possible at the same time. We experimented with these approaches using the path planner provided by our industry partner, and we show that they are able to generate scenarios that cover all the weights.