Connected and Automated Vehicles and Safety of Vulnerable Road Users: A Systems Approach

McDonald, Noreen C; Khattak, Asad J.; Combs, Tabitha S; Shay, Elizabeth · 2018 · ROSA P / Collaborative Sciences Center for Road Safety

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Summary

This report addresses the critical safety challenges facing vulnerable road users, particularly pedestrians, in the era of Connected and Automated Vehicles (CAVs). While CAVs promise to reduce human error—a factor in an estimated 94% of crashes—the authors argue that technological advances will not uniformly decrease risk. Certain environments and user groups may face elevated dangers, necessitating a systems approach that integrates vehicle technology, planning policies, and data analytics. The research aims to assess current pedestrian-vehicle conflicts, identify systemic solutions, and detect dangerous pre-crash behaviors to improve safety outcomes. The study employs a multi-pronged, trans-disciplinary methodology involving five distinct analytical efforts. First, a literature review and text mining analysis of 70 papers identified key themes regarding walkability and CAVs, using factor analysis and concept mapping to visualize connections between technology, planning, and safety. Second, the researchers utilized Fatality Accident Reporting System (FARS) data to estimate the theoretical maximum reduction in pedestrian fatalities if all vehicles were replaced by fully automated ones with current detection capabilities. Third, FARS data was also used to analyze spatial patterns of fatal pedestrian crashes across the United States to inform Vehicle-to-Pedestrian (V2P) connectivity strategies. Fourth, the study explored injury severity correlates for vulnerable users. Finally, data from the SHRP2 Naturalistic Driving Study was analyzed to determine if driving volatility serves as a leading predictor of unsafe events involving pedestrians and cyclists. Key findings indicate that the net impact of CAVs on pedestrian safety remains uncertain, with potential harm costs ranging from a 50% decrease to a 30% increase, largely due to potential increases in vehicle miles traveled. The text analysis revealed that while automation and collision avoidance are prominent topics, they are often discussed separately from planning concepts like walkability and built environment design. The FARS analysis established upper limits on the effectiveness of current pedestrian detection technologies, suggesting that technology alone cannot eliminate all risks. Furthermore, the naturalistic driving study found that intentional driving volatility prior to a crash can serve as a leading indicator for unsafe events, offering a potential metric for early warning systems in CAVs. The significance of this work lies in its comprehensive framework linking automation technology to human error and crash typologies. The authors conclude that achieving safety for vulnerable road users requires more than just vehicle automation; it demands coordinated efforts in infrastructure design, policy, and data-driven risk management. The study highlights the need for V2P technologies and planning strategies that address the specific limits of current detection systems. By identifying risky behaviors and spatial crash patterns, the research provides actionable insights for developing countermeasures that protect pedestrians and cyclists as CAVs diffuse into the transportation system.

Key finding

CAV technologies can reduce human-error crashes but may not uniformly decrease pedestrian risks due to potential increases in vehicle miles traveled and detection technology limits.

Methodology

mixed_methods

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• vru crash typology
• incidence prevalence
• demographic disparities
• pre crash contributing factors
• vru facing ehmi
• induced exposure

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: crash risk outcomes, observational prevalence
• Methodological Resource: dataset resource