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Testing autonomous vehicles (AVs) under various environmental scenarios thatlead the vehicles to unsafe situations is known to be challenging. Given theinfinite possible environmental scenarios, it is essential to find criticalscenarios efficiently. To this end, we propose a novel testing method, namedEpiTESTER, by taking inspiration from epigenetics, which enables species toadapt to sudden environmental changes. In particular, EpiTESTER adopts genesilencing as its epigenetic mechanism, which regulates gene expression toprevent the expression of a certain gene, and the probability of geneexpression is dynamically computed as the environment changes. Given differentdata modalities (e.g., images, lidar point clouds) in the context of AV,EpiTESTER benefits from a multi-model fusion transformer to extract high-levelfeature representations from environmental factors and then calculatesprobabilities based on these features with the attention mechanism. To assessthe cost-effectiveness of EpiTESTER, we compare it with a classical geneticalgorithm (GA) (i.e., without any epigenetic mechanism implemented) andEpiTESTER with equal probability for each gene. We evaluate EpiTESTER with fourinitial environments from CARLA, an open-source simulator for autonomousdriving research, and an end-to-end AV controller, Interfuser. Our results showthat EpiTESTER achieved a promising performance in identifying criticalscenarios compared to the baselines, showing that applying epigeneticmechanisms is a good option for solving practical problems.

EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism