EpiTESTER: Testing Autonomous Vehicles With Epigenetic Algorithm and Attention Mechanism

Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is challenging. Given the infinite possible environmental scenarios, it is essential to find critical scenarios efficiently. To this end, we propose a novel testing method, named
EpiTESTER
, by taking inspiration from epigenetics, which enables species to adapt to sudden environmental changes. In particular,
EpiTESTER
adopts gene silencing as its epigenetic mechanism, which regulates gene expression to prevent the expression of a certain gene, and the probability of gene expression is dynamically computed as the environment changes. Given different data modalities (e.g., images, lidar point clouds) in the context of AV,
EpiTESTER
benefits from a multi-model fusion transformer to extract high-level feature representations from environmental factors. Next, it calculates probabilities based on these features with the attention mechanism. To assess the cost-effectiveness of
EpiTESTER
, we compare it with a probabilistic search algorithm (Simulated Annealing, SA), a classical genetic algorithm (GA) (i.e., without any epigenetic mechanism implemented), and
EpiTESTER
with equal probability for each gene. We evaluate
EpiTESTER
with six initial environments from CARLA, an open-source simulator for autonomous driving research, and two end-to-end AV controllers, Interfuser and TCP. Our results show that
EpiTESTER
achieved a promising performance in identifying critical scenarios compared to the baselines, showing that applying epigenetic mechanisms is a good option for solving practical problems.