Security of Multi-Sensor Fusion Based Perception in Autonomous Driving Under Physical-World Attacks

This course focuses on understanding the security aspects of multi-sensor fusion-based perception in autonomous driving under physical-world attacks. The learning outcomes include gaining insights into the vulnerabilities of sensor fusion systems in autonomous vehicles and exploring strategies to enhance their security. The course teaches skills related to identifying potential attack vectors, implementing defense mechanisms, and evaluating the robustness of perception systems. The teaching method involves theoretical discussions and practical examples to illustrate real-world implications. The intended audience for this course includes researchers, engineers, and professionals working in the field of autonomous driving, sensor fusion, or cybersecurity.