Defending Autonomous Driving Perception against Adversarial Object-Based Attacks via Motion Planning

Autonomous vehicles (AVs) rely on perception systems to detect surrounding objects using sensors such as cameras, LiDAR (Light Detection and Ranging), and millimeter-wave (mmWave) radar. However, recent studies have shown that attackers can deceive these systems by strategically placing adversarial objects (e.g., color patches, cardboard, or metal foil) in the driving environment. These attacks pose serious safety risks, yet existing defenses primarily focus on individual sensor modalities and lack generalizability across different sensing systems. To address this gap, we propose the first generalized defense mechanism capable of mitigating various attacks using adversarial objects. Our approach integrates real-time attack detection with trajectory adaptation, guiding the victim AV to positions where the attack is less effective. The defense mechanism combines a deep reinforcement learning (DRL)-based motion planning model, which dynamically adjusts the AV's trajectory, with an uncertainty-aware filtering scheme that refines perception outputs to enhance detection robustness. Extensive experiments in both simulated and real-world environments demonstrate that our defense mechanism effectively mitigates adversarial object-based attacks across different sensing modalities and sensor fusion while maintaining safe and smooth driving behavior.