A unified theory and statistical learning approach for traffic conflict detection

This study proposes a unified theory and statistical learning approach for traffic conflict detection, addressing the long-existing call for a consistent and comprehensive methodology to evaluate the collision risk emerged in road user interactions. The proposed theory assumes a context-dependent probabilistic collision risk and frames conflict detection as estimating the risk by statistical learning from observed proximities and contextual variables. Three primary tasks are integrated: representing interaction context from selected observables, inferring proximity distributions in different contexts, and applying extreme value theory to relate conflict intensity with conflict probability. As a result, this methodology is adaptable to various road users and interaction scenarios, enhancing its applicability without the need for pre-labelled conflict data. Demonstration experiments are executed using real-world trajectory data, with the unified metric trained on lane-changing interactions on German highways and applied to near-crash events from the 100-Car Naturalistic Driving Study in the U.S. The experiments demonstrate the methodology's ability to provide effective collision warnings, generalise across different datasets and traffic environments, cover a broad range of conflicts, and deliver a long-tailed distribution of conflict intensity. This study contributes to traffic safety by offering a consistent and explainable methodology for conflict detection applicable across various scenarios. Its societal implications include enhanced safety evaluations of traffic infrastructures, more effective collision warning systems for autonomous and driving assistance systems, and a deeper understanding of road user behaviour in different traffic conditions, contributing to a potential reduction in accident rates and improving overall traffic safety.

A Comparative Conflict Resolution Dataset Derived from Argoverse-2: Scenarios with vs. without Autonomous Vehicles

As the deployment of autonomous vehicles (AVs) becomes increasingly prevalent, ensuring safe and smooth interactions between AVs and other human agents is of critical importance. In the urban environment, how vehicles resolve conflicts has significant impacts on both driving safety and traffic efficiency. To expedite the studies on evaluating conflict resolution in AV-involved and AV-free scenarios at intersections, this paper presents a high-quality dataset derived from the open Argoverse-2 motion forecasting data. First, scenarios of interest are selected by applying a set of heuristic rules regarding post-encroachment time (PET), minimum distance, trajectory crossing, and speed variation. Next, the quality of the raw data is carefully examined. We found that position and speed data are not consistent in Argoverse-2 data and its improper processing induced unnecessary errors. To address these specific problems, we propose and apply a data processing pipeline to correct and enhance the raw data. As a result, 5k+ AV-involved scenarios and 16k+ AV-free scenarios with smooth and consistent position, speed, acceleration, and heading direction data are obtained. Further assessments show that this dataset comprises diverse and balanced conflict resolution regimes. This informative dataset provides a valuable resource for researchers and practitioners in the field of autonomous vehicle assessment and regulation. The dataset is openly available via https://github.com/RomainLITUD/conflict_resolution_dataset.

Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles

Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Research in this field can be expedited with trajectory datasets collected by Autonomous Vehicles (AVs). However, trajectories collected by AVs are noisy and not readily applicable for studying CF behaviour. This paper extracts and enhances two categories of CF data, HV-following-AV (H-A) and HV-following-HV (H-H), from the open Lyft level-5 dataset. First, CF pairs are selected based on specific rules. Next, the quality of raw data is assessed by anomaly analysis. Then, the raw CF data is corrected and enhanced via motion planning, Kalman filtering, and wavelet denoising. As a result, 29k+ H-A and 42k+ H-H car-following segments are obtained, with a total driving distance of 150k+ km. A diversity assessment shows that the processed data cover complete CF regimes for calibrating CF models. This open and ready-to-use dataset provides the opportunity to investigate the CF behaviours of following AVs vs. HVs from real-world data. It can further facilitate studies on exploring the impact of AVs on mixed urban traffic.