Yes, it is theoretically and technically possible to interfere with the perception or decision-making system of autonomous vehicles by falsifying signals. This kind of attack is called Adversarial attacks, and its core is to exploit vulnerabilities in sensors, communication protocols, or algorithms that autonomous driving systems rely on. The following is the specific analysis： --- ### **1. Possible attack methods** ####**(1) Sensor spoofing** -**Camera**: Mislead the visual recognition system by projection or pasting adversarial patterns (such as special stickers, infrared light interference). For example, adding a jamming pattern to a stop sign may be misidentified as a “speed limit sign.” -**LIDAR (LiDAR)**: emits strong light pulses or false laser reflected signals to make the vehicle “see” non-existent obstacles or ignore real obstacles. -**Millimeter wave radar**: By emitting electromagnetic interference in the same frequency band, the radar misjudges the distance or speed. ####**(2) Positioning and navigation attacks** -**GPS spoofing**: Falsifying GPS signals to offset the positioning of the vehicle (such as making the vehicle mistakenly think it is in the wrong lane or intersection). - **High-precision map tampering**: By invading the map service provider or the Internet of Vehicles (V2X) system, injecting false road information. ####**(3)Communication protocol attack** -**V2X attack**: Falsify the communication signals between the vehicle and the infrastructure (V2I) or other vehicles (V2V), such as sending false emergency braking instructions or traffic light status. -**OTA update hijacking**: By tampering with the wireless software update package, malicious code is implanted to destroy the decision logic. ####**4)Data pollution** -**Training data poisoning**: Malicious data is injected into the training phase of the autonomous driving model, causing the model to learn incorrect rules (for example, the red light is recognized as a green light). --- ### **2. Technical principles and cases** -**Adversarial Examples**: Deceive the deep learning model through subtle disturbances. For example, MIT research has caused Tesla Autopilot to stray into the wrong lane by pasting a specific pattern on the road surface. -**Examples of physical attacks**: Experiments in 2019 showed that laser interference with lidar can enable vehicles to detect ”ghost obstacles" and brake urgently. -**GPS deception actual combat**: In 2013, a team from the University of Texas successfully deceived the navigation system of a luxury yacht with a device worth 2,000 U.S. dollars. --- ### **3. Defensive measures** -Sensor redundancy and fusion**: Combined with cross-verification of multi-source data such as cameras, radars, and lidars, the risk of a single sensor being deceived is reduced. -**Signal encryption and authentication**: Encryption protocols (such as IEEE 1609.2 standard) are used for V2X communication and GPS signals to prevent forgery. -**Anomaly detection algorithm**: Real-time monitoring of the rationality of sensor data (for example, manual takeover is triggered when a “ghost obstacle” suddenly appears). -**Confrontation training**: Introduce confrontation samples during the model training phase to improve robustness. -**Hardware protection**: Use anti-jamming lidar (such as FM continuous wave FMCW LiDAR) or anti-light pollution camera filters. --- ### **4. Status quo and challenges** -**High attack cost**: Most attacks require close physical contact or professional equipment, making large-scale attacks more difficult. -**Promotion of industry standards**: Automotive network security standards such as ISO/SAE 21434 are being improved, and it is mandatory for car companies to design defense mechanisms. -**Ethics and regulations**: Countries have begun to enact legislation to combat interference with autonomous driving (for example, the U.S. "Autonomous Driving Act" clarifies network security requirements). --- ###**Conclusion** Although the autonomous driving system is at risk of being attacked by forged signals, through technological iteration and regulatory improvement, the autonomous driving system is at risk of being attacked by forged signals.