Still Camouflage, Moving Illusion: View-Induced Trajectory Manipulation in Autonomous Driving
This work introduces a novel, easily deployable attack that turns a known challenge (viewing-angle variation) into an attack tool, posing a new threat to vision-based autonomous driving systems.
The authors propose a new adversarial attack paradigm for autonomous driving where a static, passive adversarial camouflage on a vehicle exploits viewing-angle variation to induce consistent feature drift across frames, causing the system to infer a false trajectory (e.g., a cut-in) and trigger unnecessary braking. They achieve an end-to-end success rate of up to 87.5% in causing hard-braking events on the nuScenes dataset.
Existing physical adversarial attacks on vision-based autonomous driving induce time-evolving perception errors, including biased object tracking or trajectory prediction, through (i) sophisticated physical patch inducing detection box drift when entering the view distance, or (ii) dynamically changing patches that cause different perception errors at different time. In both cases, viewing-angle variation is treated as a challenge, requiring adversarial patches to remain effective across frames under varying views, leading to complex multi-view optimization. In contrast, we show that viewing-angle variation itself can be turned into an attack tool. We design a new attack paradigm where a static, passive adversarial camouflage is mounted on a vehicle whose view-dependent appearance naturally evolves with relative motion, inducing consistent feature drift across frames. This causes the system to infer a physically plausible but incorrect trajectory, such as a false cut-in, which propagates to downstream decision-making and triggers unnecessary braking. Unlike prior approaches that require multi-view robustness or active intervention, our attack emerges from normal driving dynamics and is easy to deploy: a parked vehicle with a natural camouflage can induce hard braking in passing autonomous vehicles. We demonstrate the novel attack on nuScenes dataset, showing the effectiveness with an end-to-end success rate of up to 87.5%, measured by hard-braking events, and robustness across different scene backgrounds, victim vehicle speeds, and perception models.