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The vision-based perception modules in autonomous vehicles (AVs) are prone to physical adversarial patch attacks. However, most existing attacks indiscriminately affect all passing vehicles. This paper introduces L-HAWK, a novel controllable physical adversarial patch activated by long-distance laser signals. L-HAWK is designed to target specific vehicles when the adversarial patch is triggered by laser signals while remaining benign under normal conditions. To achieve this goal and address the unique challenges associated with laser signals, we propose an asynchronous learning method for L-HAWK to determine the optimal laser parameters and the corresponding adversarial patch. To enhance the attack robustness in real-world scenarios, we introduce a multi-angle and multi-position simulation mechanism, a noise approximation approach, and a progressive sampling-based method. L-HAWK has been validated through extensive experiments in both digital and physical environments. Compared to a 59% success rate of TPatch (Usenix ’23) at 7 meters, L-HAWK achieves a 91.9% average attack success rate at 50 meters. This represents a 56% improvement in attack success rate and a more than sevenfold increase in attack distance.