Interaction Dataset of Autonomous Vehicles with Traffic Lights and Signs

2025-01-21

AI Summary

Current autonomous vehicle (AV) research is severely constrained by the scarcity of high-quality, fine-grained trajectory datasets capturing interactions with traffic control devices (TCDs), particularly traffic lights and stop signs. To address this gap, we introduce the first large-scale, real-world AV–TCD interaction trajectory dataset—comprising over 81,000 high-fidelity samples—derived from Waymo Motion data, including 37,000 traffic-light and 44,000 stop-sign interaction instances. We propose a novel, rule-based taxonomy for classifying TCD interactions and integrate wavelet-based signal denoising with multi-dimensional trajectory quality assessment to suppress acceleration and jerk anomalies to below 0.02%. This dataset fills a critical void in the public domain, enabling more accurate AV behavioral modeling and significantly enhancing simulation fidelity and reliability.

Technology Category

• Application Domains: Transportation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Humans and AI: Interaction Techniques and Devices

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Autonomous Vehicles
• Traffic Signals
• Data Sets

Innovation

• Autonomous Vehicles
• Traffic Signal Interaction
• Real-world Scenarios