Benchmarking Tesla's Traffic Light and Stop Sign Control: Field Dataset and Behavior Insights
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911316456767488 |
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| author | Li, Zheng Zhang, Peng Liang, Shixiao Zhou, Hang Ma, Chengyuan Yao, Handong Li, Qianwen Li, Xiaopeng |
| author_facet | Li, Zheng Zhang, Peng Liang, Shixiao Zhou, Hang Ma, Chengyuan Yao, Handong Li, Qianwen Li, Xiaopeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11802 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking Tesla's Traffic Light and Stop Sign Control: Field Dataset and Behavior Insights Li, Zheng Zhang, Peng Liang, Shixiao Zhou, Hang Ma, Chengyuan Yao, Handong Li, Qianwen Li, Xiaopeng Robotics Computer Vision and Pattern Recognition Human-Computer Interaction 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. |
| title | Benchmarking Tesla's Traffic Light and Stop Sign Control: Field Dataset and Behavior Insights |
| topic | Robotics Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2512.11802 |