Effects of automatic emergency braking systems to reduce risk of crash and serious injuries among pedestrians and bicyclists

Amin, Khabat; Kullgren, Anders; Tingvall, Claes · 2025 · openalex_search

DOI: 10.55329/nbqj7880

archive: archived pipeline: cataloged verified

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Summary

This study investigates the real-world effectiveness of Automatic Emergency Braking (AEB) systems with pedestrian and bicyclist detection in reducing crash risks and mitigating injury severity. Motivated by the disproportionate share of fatalities and serious injuries experienced by vulnerable road users (VRUs) despite overall declines in traffic deaths, the research aims to quantify AEB performance under various conditions and determine if the systems successfully reduce injury severity when collisions occur. The analysis utilized data from the Swedish Traffic Accident Data Acquisition (STRADA) database, covering police-reported and hospital-reported crashes between 2012 and 2022 involving cars from model years 2012–2022. The dataset included 2,160 pedestrian collisions, 3,374 bicyclist collisions, and 5,738 rear-end crashes serving as a non-sensitive control group. Researchers employed an induced exposure approach using odds ratio calculations to assess crash reduction, comparing sensitive crashes (VRU strikes) against non-sensitive crashes (rear-end strikes). Injury mitigation was evaluated by comparing the proportion of injuries with a Maximum Abbreviated Injury Scale (MAIS) of 3+ and the Risk of Permanent Medical Impairment (RPMI) between vehicles with and without AEB. Results indicated an overall crash risk reduction of approximately 20% for vehicles equipped with AEB for pedestrians or bicyclists. Effectiveness varied by condition: reductions were observed in daylight and darkness, though the latter was not statistically significant. AEB with pedestrian detection showed significant crash reduction during rain, fog, and snow, whereas cyclist detection did not show significant reduction under these adverse weather conditions. AEB for bicyclists was more effective on high-speed roads (60–120 km/h) than low-speed roads, while pedestrian detection showed similar reductions across speeds. Notably, AEB for pedestrians was most effective at intersections, while AEB for bicyclists was most effective on straight roads. Crucially, the study found no statistically significant difference in injury severity (MAIS3+ or RPMI) between crashes involving vehicles with and without AEB. The authors conclude that while AEB systems demonstrate potential in reducing crash frequency, their current effectiveness is insufficient to provide adequate protection at existing speed limits, and there is a significant gap between predicted potential and real-world performance. The lack of verified injury mitigation suggests that in most collisions, AEB systems either failed to activate or did not brake sufficiently to reduce impact speed. The study highlights the need for improved system performance, particularly in detecting VRUs and reducing impact severity, and advocates for a holistic road safety approach integrating vehicle technology, speed management, and infrastructure improvements.

Key finding

Automatic Emergency Braking systems with pedestrian and bicyclist detection reduce the risk of collisions by approximately 20%, but they do not significantly mitigate injury severity when crashes do occur.

Methodology

dataset

Sample size: 11272

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via scout_discovery on 2026-05-08 (2 acquisition events logged).

StageOutcomeToolModelPromptAttemptsCompleted
discover partial scout 2 2026-05-08
archive success unpaywall 2 2026-06-06
extract success cached 3 2026-06-10
clean success clean 1 2026-06-04
chunk success chunk 1 2026-06-04
embed success embed Qwen/Qwen3-Embedding-8B 1 2026-06-04
enrich success 1 2026-05-08
promote success 1 2026-06-04
summarize success llm qwen3.6-27b-prismaquant summ-v5 2 2026-06-10
tag success vector_similarity 15 2026-06-11
verify success 2 2026-06-10

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

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