From Roadside Sensing to Risk-Informed Safety Control: Surrogate Safety Measures as a Cyber-Physical Interface

Abstract

Proactive intersection safety analysis depends on trajectory data whose reliability varies by sensing modality, yet infrastructure-perception and surrogate safety measure (SSM) research have only recently begun to converge. This paper synthesizes both literatures alongside control theory to clarify how sensing choices, safety metrics, and predictive control connect end-to-end, and where infrastructure-derived risk signals could inform autonomous vehicle decision-making. Searches covered light detection and ranging (LiDAR), cooperative perception, time-to-collision (TTC), post-encroachment time (PET), extreme value theory, model predictive control (MPC), and signal control. Screening considered trajectory-based safety and control relevance, excluding detection benchmarks without safety linkage. This structured narrative synthesis retained 56 sources. No single SSM is universally valid: one-dimensional TTC and modified TTC suit longitudinal conflicts, two-dimensional TTC extends time-based measurement to crossing geometries, and observed PET captures crossing and vulnerable road user (VRU) conflicts but only after the event. LiDAR consistently outperforms camera-only sensing for continuous three-dimensional trajectory extraction, illumination robustness, and privacy, though its range degrades in heavy rain, fog, and snow. Its output becomes control-ready only when paired with filtering–Kalman, Savitzky-Golay–that stabilizes noisy detections. This review complements it from the infrastructure side: it examines how roadside sensing affects that chain's reliability, classifies SSMs by conflict geometry and temporal availability, and maps risk components onto control objectives and constraints for infrastructure and connected and automated vehicle (CAV) applications.