To Go or Not to Go: Shedding Light on Traffic Light Signal Manipulation and Defense Strategies

Connected autonomous vehicles must accurately detect, and adhere, to traffic light signals to ensure safe and efficient traffic flow. Misinterpretation of traffic lights can result in potential safety issues for drivers and pedestrians. Recent work demonstrated attacks that projected structured light patterns onto vehicle cameras, causing traffic signs and traffic light color misinterpretation. In this work, we characterize a novel vulnerability of traffic light physical structures that can be exploited by attackers to deceive recognition systems. When visible and invisible laser light is projected onto traffic lights, it is scattered by its internal reflectors. To a vehicle’s camera, the reflected light appears the same as a genuine light source, resulting in dangerous red and green traffic light status misclassifications. We evaluate our attack against three state-of-the-art traffic light recognition models and show successful misclassification up to 25 meters from the target traffic light. Furthermore, the attack succeeds both in daytime and nighttime conditions both in static and moving vehicle scenarios up to 10 Km/h speed. To mitigate this threat, we propose a detection system based on light texture patterns that achieve 100% TPR and 1.8% FPR in our real-world scenarios