分析特斯拉交通灯识别系统实测行为，揭示其停车与跟车规律。
Benchmarking Tesla's Traffic Light and Stop Sign Control: Field Dataset and Behavior Insights
- 通过实地实验采集车辆轨迹与视频数据，建立交互行为分类体系。
- 发现跟车阈值约90米，停车响应对速度偏差和相对速度敏感。
- 结果可支持自动驾驶系统仿真与安全评估，适合研究者使用。
理解高级驾驶辅助系统（ADAS）与交通控制装置（TCD）的交互对评估其对交通运行的影响至关重要，但此类交互尚未得到充分的实证研究。本文针对成熟的特斯拉交通灯与停车标志控制系统（TLSSC），设计并执行了在不同限速和TCD类型下的实地实验，收集同步的高分辨率车辆轨迹数据与驾驶员视角视频。基于这些数据，我们构建了TLSSC-TCD交互行为的分类体系（包括停车、加速与跟车），并校准全速度差模型（FVDM）以量化各行为模式。一个新发现是存在约90米的跟车阈值。校准结果显示，停车行为对期望速度偏差和相对速度响应强烈，而加速行为更为保守；交叉口跟车行为动态更平稳，车距更紧密，优于常规跟车。所建立的数据集、行为定义与模型表征共同为未来仿真、安全评估及ADAS-TCD交互逻辑设计奠定了基础。数据集已公开于GitHub。
原文摘要 · Abstract (English)
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.
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