Benchmarking Tesla's Traffic Light and Stop Sign Control: Field Dataset and Behavior Insights

TL;DR

This paper provides a comprehensive field dataset and behavioral analysis of Tesla's traffic light and stop sign control system, revealing key interaction behaviors and calibration models that inform future ADAS safety and design improvements.

Contribution

It introduces a novel empirical dataset, behavior taxonomy, and calibration of the FVDM for Tesla's TLSSC, advancing understanding of ADAS-TCD interactions.

Findings

Identified a car-following threshold of approximately 90 meters.

Stopping behavior is highly responsive to speed deviation and relative speed.

Intersection car-following shows smoother dynamics and tighter headways.

Abstract

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 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. A novel empirical insight is the identification of a car-following threshold (~90 m). Calibration results reveal that stopping behavior is driven by strong responsiveness to both desired speed deviation and relative speed, whereas accelerating behavior is more conservative. Intersection car-following behavior exhibits smoother dynamics and tighter headways compared to standard car-following behaviors. 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.