Here are some theoretical discussions: In recent years, academia and industry have indeed conducted a lot of research on the safety of autonomous driving systems, including the exploration of possible interference and deception of sensor systems (such as GPS, radar, LiDAR, and cameras). For example: 1. GPS signal forgery: Studies have shown that by sending forged GPS signals to the victim device, the device may obtain incorrect location information. However, in practical applications, modern vehicles often use multiple data sources (such as inertial navigation, map matching, other satellite systems, etc.) to cross-verify location information, thereby reducing the risk of interference to a single sensor. 2. Adversarial attacks: Some academic work has explored how to provide carefully designed adversarial samples to camera or LiDAR systems, which may cause computer vision algorithms to make wrong judgments. But similarly, actual systems often use multi-sensor fusion technology and redundant design to improve overall robustness and safety. 3. Multi-sensor redundancy and data fusion: In the design of modern autonomous driving vehicles, information from multiple sensors is integrated, and data fusion algorithms are used to identify and eliminate abnormal situations. Therefore, it is usually difficult for a single signal to forge a decisive impact on the entire system unless multiple sensors are successfully deceived at the same time, and there may be anomaly detection and redundant safety mechanisms in the system. It should be emphasized that although some potential security vulnerabilities may be discovered under theoretical and laboratory conditions, in reality: • Any deliberate use of such vulnerabilities for improper purposes is illegal and extremely dangerous. • Automakers and autonomous driving system developers are constantly updating protection technologies to deal with possible attack methods. • Academic research on such issues is mainly to reveal system weaknesses and promote security improvements, rather than to provide guidance for actual attacks. In summary, it is theoretically conceivable to influence the response of autonomous vehicles by forging or manipulating certain signals, but the actual situation is extremely complex, and modern systems have multiple levels of protection against such attacks. Any attempt to use this knowledge for illegal behavior is illegal and unethical, and discussion of these issues is limited to the academic and defense research fields, with the goal of improving system security.