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Autonomous vehicles rely on LiDAR sensors to detect obstacles such aspedestrians, other vehicles, and fixed infrastructures. LiDAR spoofing attackshave been demonstrated that either create erroneous obstacles or preventdetection of real obstacles, resulting in unsafe driving behaviors. In thispaper, we propose an approach to detect and mitigate LiDAR spoofing attacks byleveraging LiDAR scan data from other neighboring vehicles. This approachexploits the fact that spoofing attacks can typically only be mounted on onevehicle at a time, and introduce additional points into the victim's scan thatcan be readily detected by comparison from other, non-modified scans. Wedevelop a Fault Detection, Identification, and Isolation procedure thatidentifies non-existing obstacle, physical removal, and adversarial objectattacks, while also estimating the actual locations of obstacles. We propose acontrol algorithm that guarantees that these estimated object locations areavoided. We validate our framework using a CARLA simulation study, in which weverify that our FDII algorithm correctly detects each attack pattern.

Cooperative Perception for Safe Control of Autonomous Vehicles under LiDAR Spoofing Attacks