Are Existing Road Design Guidelines Suitable for Autonomous Vehicles?

The emergence of Autonomous Vehicles (AVs) has spurred research into testing the resilience of their perception systems, i.e. to ensure they are not susceptible to making critical misjudgements. It is important that they are tested not only with respect to other vehicles on the road, but also those objects placed on the roadside. Trash bins, billboards, and greenery are all examples of such objects, typically placed according to guidelines that were developed for the human visual system, and which may not align perfectly with the needs of AVs. Existing tests, however, usually focus on adversarial objects with conspicuous shapes/patches, that are ultimately unrealistic given their unnatural appearances and the need for white box knowledge. In this work, we introduce a black box attack on the perception systems of AVs, in which the objective is to create realistic adversarial scenarios (i.e. satisfying road design guidelines) by manipulating the positions of common roadside objects, and without resorting to `unnatural' adversarial patches. In particular, we propose TrashFuzz , a fuzzing algorithm to find scenarios in which the placement of these objects leads to substantial misperceptions by the AV -- such as mistaking a traffic light's colour -- with overall the goal of causing it to violate traffic laws. To ensure the realism of these scenarios, they must satisfy several rules encoding regulatory guidelines about the placement of objects on public streets. We implemented and evaluated these attacks for the Apollo, finding that TrashFuzz induced it into violating 15 out of 24 different traffic laws.