Automated testing for self-driving cars using real-world roads

Abstract

Cyber-physical systems are complex systems constructed from different independent parts. A self-driving car is an example of a cyber-physical system where independent parts have to come together in order to result in a car that is able to drive by itself. The main challenge is finding failures within the interactions between the independent parts of the self-driving system. In this paper, we present a novel algorithm REWOSA, in order to detect these faults within the self-driving software. With the use of real-world roads extracted from Google Maps combined with a multi-objective genetic algorithm, we develop a new way to generate roads for testing self-driving cars. We evaluate this algorithm against a state-of-the-art multi-objective genetic algorithm using randomly generated roads using two different setups. Our results show that REWOSA is able to generate more failures than the baseline on both the setups, as well as create more complex roads. In return, REWOSA does create a large overhead due to the complexity of the real-world roads. However, this overhead is justifiable as we can detect more faults with the more complex real-world roads.