Connected-Vehicle Telemetry for Crash-Risk Reduction and Road Safety

Abstract

Connected-vehicle telemetry for crash-risk reduction and road safety refers to the collection, transmission, and analysis of data from vehicles and roadside sensing to monitor traffic conditions, detect imminent hazards, and support proactive safety interventions within intelligent transportation systems (ITS). By coupling vehicle dynamics, infrastructure signals, and environmental context, the field aims to reduce crash risk through real-time warnings, adaptive operations, and data-driven policy insights. The topic is notable for its potential to lower injury and fatality rates, improve mobility, and inform infrastructure planning, even as deployments confront technical, governance, and public-trust challenges (arXiv, 2025; Federal Highway Administration, n.d.-a; University of Florida, n.d.). The field rests on a multi-layer data and technology stack, drawing on vehicle telemetry, vehicle-to-everything (V2X) communications, cameras, detectors, and high-definition maps to enable real-time monitoring and near-term decision-making at traffic-management centers and within onboard systems (arXiv, 2025; Federal Highway Administration, n.d.-a). Standards and interoperability (e.g., SAE J2735/J2945, IEEE 1609) provide the backbone for cross-vehicle and cross-infrastructure exchange, while privacy, security, and governance considerations shape how data are collected, shared, and protected (Consumer Reports, n.d.; Federal Highway Administration, n.d.-a; Systems Approach, n.d.; Virginia Tech Transportation Institute, n.d.). Practical deployments increasingly combine edge computing with cloud analytics to deliver low-latency risk scoring, near-crash detection, and context-aware warnings, though integration across manufacturers and networks remains a major hurdle (Countly, n.d.; Expanso, n.d.; Federal Highway Administration, n.d.-a; Systems Approach, n.d.). Evidence on effectiveness is mixed and actively debated. Some field programs report reductions in crash risk and improvements in safety indicators when CV-enabled interventions are deployed, while others show limited mobility gains or highlight the challenges of scaling, latency, data provenance, and vendor interoperability at scale (arXiv, 2024b; GoFleet, n.d.; ISS Group, n.d.; Systems Approach, n.d.). Alongside safety benefits, critical controversies include proving causal links between surrogate safety measures and actual crashes, ensuring equitable protection across populations, and addressing privacy and consent concerns as telemetry data become increasingly pervasive in insurance, enforcement, and governance contexts (MITRE, 2023; ResearchGate, n.d.-a; Smarter Center, n.d.; SMATS Traffic, n.d.). Beyond technical deployment, researchers and policymakers emphasize the need for robust data governance, transparent evaluation frameworks, and responsible data stewardship. Public pilots, open data initiatives, and standards collaborations (including SENSORIS, CV pilots, and multi-site deployments) illustrate ongoing efforts to harmonize data models, ensure trustworthy provenance, and translate telemetry insights into safer roads while maintaining driver trust and minimizing surveillance risks (MITRE, 2023; National Highway Traffic Safety Administration, n.d.; National Library of Medicine, n.d.-a; Systems Approach, n.d.). As the ecosystem evolves, the field continues to balance safety gains with practical considerations of scalability, privacy, security, and social acceptance (Automotive Manufacturing Solutions, n.d.; Emergent Mind, n.d.; Smarter Center, n.d.).