  Autonomous vehicles rely on LiDAR sensors to detect obstacles such as
pedestrians, other vehicles, and fixed infrastructures. LiDAR spoofing attacks
have been demonstrated that either create erroneous obstacles or prevent
detection of real obstacles, resulting in unsafe driving behaviors. In this
paper, we propose an approach to detect and mitigate LiDAR spoofing attacks by
leveraging LiDAR scan data from other neighboring vehicles. This approach
exploits the fact that spoofing attacks can typically only be mounted on one
vehicle at a time, and introduce additional points into the victim's scan that
can be readily detected by comparison from other, non-modified scans. We
develop a Fault Detection, Identification, and Isolation procedure that
identifies non-existing obstacle, physical removal, and adversarial object
attacks, while also estimating the actual locations of obstacles. We propose a
control algorithm that guarantees that these estimated object locations are
avoided. We validate our framework using a CARLA simulation study, in which we
verify that our FDII algorithm correctly detects each attack pattern.
