GCP: Guarded Collaborative Perception With Spatial-Temporal Aware Malicious Agent Detection

Connected autonomous vehicles share sensor data to improve driving safety, but this collaboration is vulnerable to adversarial message attacks (malicious input designed to fool AI systems) from bad actors that can degrade performance. The paper describes a new blind area confusion attack that bypasses existing defenses, then proposes GCP, a framework that detects malicious agents by checking both spatial consistency (whether sensor readings from different vehicles agree) and temporal anomalies (unusual patterns over time) using statistical testing methods.