Benchmarking Tesla’s Traffic Light and Stop Sign Control: Field dataset and behavior insights

Understanding how Advanced Driver-Assistance Systems (ADAS) interact with Traffic Control Devices (TCDs) is critical for assessing their influence on traffic operations, yet this interaction has received little focused empirical study. This paper presents a field dataset and behavioral analysis of Tesla’s Traffic Light and Stop Sign Control (TLSSC), a mature ADAS that perceives traffic lights and stop signs. We design and execute nighttime public-road experiments across varied speed limits and TCD types, collecting synchronized high-resolution vehicle trajectory data and driver-perspective video. From these data, we develop a taxonomy of TLSSC–TCD interaction behaviors (i.e., stopping, accelerating, and car following) and calibrate the Full Velocity Difference Model (FVDM) to quantitatively characterize each behavior mode. Calibration results reveal that stopping behavior exhibits stronger responsiveness to both the optimal-speed term and relative-speed term, whereas accelerating behavior is more gradual and conservative. Compared with standard car-following behavior, intersection car-following generally shows weaker relative-speed responsiveness and tighter spacing characteristics. The established dataset, behavior definitions, and model characterizations together provide a foundation for future simulation, safety evaluation, and design of ADAS–TCD interaction logic. Our dataset is available at Github .