  Testing autonomous vehicles (AVs) under various environmental scenarios that
lead the vehicles to unsafe situations is known to be 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 and then calculates
probabilities based on these features with the attention mechanism. To assess
the cost-effectiveness of EpiTESTER, we compare it with a classical genetic
algorithm (GA) (i.e., without any epigenetic mechanism implemented) and
EpiTESTER with equal probability for each gene. We evaluate EpiTESTER with four
initial environments from CARLA, an open-source simulator for autonomous
driving research, and an end-to-end AV controller, Interfuser. 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.
