Cyberattack Resilience of Autonomous Vehicle Sensor Systems: Evaluating RGB vs. Dynamic Vision Sensors in CARLA

Autonomous Vehicles (AVs) rely on a heterogeneous sensor suite RGB cameras, LiDAR, GPS/IMU, and emerging event-based Dynamic Vision Sensors (DVS) to perceive and navigate complex environments. However, these sensors can be deceived by realistic cyberattacks, undermining safety. In this work, we systematically implement seven attack vectors in the CARLA simulator, salt and pepper noise, event flooding, depth map tampering, LiDAR phantom injection, GPS spoofing, denial of service, and steering bias control, and measure their impact on a state-of-the-art end to end driving agent. We then equip each sensor with tailored defenses (e.g., adaptive median filtering for RGB, spatial clustering for DVS) and integrate a semi-supervised anomaly detector (EfficientAD from anomalib) trained exclusively on benign data. Our detector achieves clear separation between normal and attacked conditions (mean RGB anomaly scores of 0.00 vs. 0.38; DVS: 0.61 vs. 0.76), yielding over 95% detection accuracy with fewer than 5% false positives. Defense evaluations reveal that GPS spoofing is fully mitigated, whereas RGB and depth based attacks still induce 30–45% trajectory drift despite filtering. Notably, DVS sensors exhibit greater intrinsic resilience in high dynamic range scenarios, though their asynchronous output necessitates carefully tuned thresholds. These findings underscore the critical role of multi-modal anomaly detection and demonstrate that possibility of integrating DVS alongside conventional sensors significantly strengthens AV cybersecurity.