Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2026

Abstract

The emergence of Autonomous Vehicles (AVs) has spurred research into testing the resilience of their perception systems, i.e., ensuring that they are not susceptible to critical misjudgements. It is important that these systems are tested not only with respect to other vehicles on the road, but also with respect to objects placed on the roadside. Trash bins, billboards, and greenery are examples of such objects, typically positioned according to guidelines developed for the human visual system, which may not align perfectly with the needs of AVs. Existing tests, however, usually focus on adversarial objects with conspicuous shapes or patches, which are ultimately unrealistic due to their unnatural appearance and reliance on white-box knowledge. In this work, we introduce a black-box attack on AV perception systems that creates 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 that finds scenarios in which the placement of these objects leads to substantial AV misperceptions—such as mistaking a traffic light’s colour—with the overall goal of causing traffic-law violations. To ensure realism, these scenarios must satisfy several rules encoding regulatory guidelines governing the placement of objects on public streets. We implemented and evaluated these attacks on the Apollo autonomous driving system, finding that TrashFuzz induced violations of 15 out of 24 traffic laws.

Keywords

Autonomous vehicles, perception systems, fuzz testing, adversarial scenarios, safety-critical systems

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Areas of Excellence

Digital transformation

Publication

Proceedings of the 19th IEEE International Conference on Software Testing, Verification and Validation (ICST 2026), Daejeon, South Korea, May 18-22

First Page

40

Last Page

51

ISBN

9798319533098

Identifier

10.1109/ICST69053.2026.00019

Publisher

IEEE

City or Country

Los Alamitos, CA

Additional URL

https://doi.org/10.1109/ICST69053.2026.00019

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