# Interaction Dataset of Autonomous Vehicles with Traffic Lights and Signs

arXiv: https://arxiv.org/abs/2501.12536 · HTML: https://mlsanity.com/paper/2501.12536/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 9 of 9)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [One Thousand and One Hours: Self-driving Motion Prediction Dataset](https://mlsanity.com/paper/2006.14480/) (2020)
- [openACC. An open database of car-following experiments to study the properties of commercial ACC systems](https://mlsanity.com/paper/2004.06342/) (2020)
- [Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles](https://mlsanity.com/paper/2305.18921/) (2023)
- [A Unified Longitudinal Trajectory Dataset for Automated Vehicle](https://mlsanity.com/paper/2406.00009/) (2024)
- [Assessing Markov Property in Driving Behaviors: Insights from Statistical Tests](https://mlsanity.com/paper/2501.10625/) (2025)
- [Adaptive Smoothing for Trajectory Reconstruction](https://mlsanity.com/paper/1803.07184/) (2018)

## Built on by (most cited first, 2 of 2)

- [SVBRD-LLM: Self-Verifying Behavioral Rule Discovery for Autonomous Vehicle Identification](https://mlsanity.com/paper/2511.14977/) (2025)
- [Benchmarking Tesla's Traffic Light and Stop Sign Control: Field Dataset and Behavior Insights](https://mlsanity.com/paper/2512.11802/) (2025)

## Abstract (arXiv)

This paper presents the development of a comprehensive dataset capturing interactions between Autonomous Vehicles (AVs) and traffic control devices, specifically traffic lights and stop signs. Derived from the Waymo Motion dataset, our work addresses a critical gap in the existing literature by providing real-world trajectory data on how AVs navigate these traffic control devices. We propose a methodology for identifying and extracting relevant interaction trajectory data from the Waymo Motion dataset, incorporating over 37,000 instances with traffic lights and 44,000 with stop signs. Our methodology includes defining rules to identify various interaction types, extracting trajectory data, and applying a wavelet-based denoising method to smooth the acceleration and speed profiles and eliminate anomalous values, thereby enhancing the trajectory quality. Quality assessment metrics indicate that trajectories obtained in this study have anomaly proportions in acceleration and jerk profiles reduced to near-zero levels across all interaction categories. By making this dataset publicly available, we aim to address the current gap in datasets containing AV interaction behaviors with traffic lights and signs. Based on the organized and published dataset, we can gain a more in-depth understanding of AVs' behavior when interacting with traffic lights and signs. This will facilitate research on AV integration into existing transportation infrastructures and networks, supporting the development of more accurate behavioral models and simulation tools.