AsFault: Testing Self-Driving Car Software Using Search-Based Procedural Content Generation

Abstract

Ensuring the safety of self-driving cars is important, but neither industry nor authorities have settled on a standard way to test them. Deploying self-driving cars for testing in regular traffic is a common, but costly and risky method, which has already caused fatalities. As a safer alternative, virtual tests, in which self-driving car software is tested in computer simulations, have been proposed. One cannot hope to sufficiently cover the huge number of possible driving situations self-driving cars must be tested for by manually creating such tests. Therefore, we developed AsFault, a tool for automatically generating virtual tests for systematically testing self-driving car software. We demonstrate AsFault by testing the lane keeping feature of an artificial intelligence-based self-driving car software, for which AsFault generates scenarios that cause it to drive off the road. A video illustrating AsFault in action is available at: https://youtu.be/lJ1sa42VLDw