LiDAR in Connected and Autonomous Vehicles: Perception, Threat Model, and Defense

Connected and Autonomous Vehicles (CAVs) are referred to as self-driving vehicles that will become an essential component of future intelligent transportation systems. These CAVs will be equipped with various sensors for perceiving their surroundings and onboard computing capabilities to process sensor data in real-time. Light Detection and Ranging (LiDAR) is one of the essential sensors used for detecting objects and accurate distance estimation. However, LiDAR sensors are susceptible to several types of attacks. Adversaries can exploit LiDAR sensors either physically, by sending signals directly to the sensor, or digitally, by manipulating LiDAR data after gaining access to the in-vehicle network. Over the past few years, there has been significant research on the vulnerabilities, attack models, and security of LiDAR sensors. However, to our knowledge, no comprehensive survey exists that addresses these aspects of autonomous vehicle security. This paper aims to bridge this gap by presenting an overview of LiDAR-based perception, data processing, threat models, and defense mechanisms for LiDAR sensors in CAVs. We believe this paper will serve as a valuable reference for researchers, providing a clear understanding of cyber-physical attacks and defense strategies related to LiDAR sensors in autonomous vehicles and related fields.