Roadside LiDAR for Cooperative Safety Auditing at Urban Intersections: Toward Auditable V2X Infrastructure Intelligence

TL;DR

This paper introduces a roadside LiDAR framework for urban intersection safety analysis, combining trajectory data, human-in-the-loop QA, and interpretability to provide defensible safety evidence and improve perception system auditing.

Contribution

It presents a novel infrastructure-assisted safety auditing framework using roadside LiDAR, integrating iterative QA and near-miss analytics for urban intersection safety assessment.

Findings

Time-to-collision drops below 1s for heavy vehicle-bicycle interactions.

Framework reduces failure modes like track fragmentation and false conflicts.

LiDAR-based auditing can identify high-risk interactions beyond crash data.

Abstract

Urban intersections expose the limitations of single-vehicle perception under occlusion and partial observability. In this study, we present an auditable roadside LiDAR framework for infrastructure-assisted safety analysis at a signalized urban intersection in New York City, developed and evaluated using real-world data. The proposed framework integrates trajectory construction, iterative human-in-the-loop quality assurance (QA), and interpretable near-miss analytics to produce defensible safety evidence from infrastructure sensing. Using a human-labeled heavy vehicle--bicycle interaction as an anchor case, we show that direction-agnostic time-to-collision (TTC) drops below 1s, while longitudinal TTC remains above conservative braking thresholds, revealing a lateral-intrusion-dominated conflict mechanism. Beyond individual cases, continuous-window evaluation and multi-round QA analysis demonstrate that the framework systematically reduces failure modes such as track fragmentation, spurious TTC triggers, unstable geometry, and cross-lane false conflicts. These results position roadside LiDAR as a practical post-hoc auditing mechanism for cooperative perception systems, with broader statistical validation discussed. This work provides a pathway toward scalable, data-driven safety auditing of urban intersections, enabling transportation agencies to identify and mitigate high-risk interactions beyond crash-based analyses.