Characteristics of Rear-End Collisions: A Comparison between Automated Driving System-Involved Crashes
DOI: 10.1177/03611981231209319
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Summary
This study investigates the characteristics of rear-end collisions involving vehicles equipped with Automated Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS). Motivated by the increasing prevalence of these technologies and the observation that rear-end crashes dominate ADS-involved incidents, the research aims to compare crash patterns between ADS and ADAS vehicles and identify contributing factors. While previous studies focused on ADS versus human-driven vehicles, this work addresses a gap in the literature by directly comparing ADS and ADAS crash dynamics, particularly regarding rear-end collisions, which are critical for understanding safety in mixed traffic environments. The researchers utilized a dataset from the National Highway Traffic Safety Administration (NHTSA) covering crashes reported between July 2021 and May 2022. After preprocessing to remove invalid records and missing data, the final sample consisted of 130 ADS-involved crashes and 84 ADAS-involved crashes. The study defined rear-end collisions as instances where the subject vehicle was struck from behind. Key variables included vehicle type, speed gap ratio (the difference between the subject vehicle’s speed and the posted speed limit), pre-crash movement of the crash partner, roadway type, incident time, roadway surface, lighting, and crash partner type. Three binomial logistic regression models were constructed: one to compare the likelihood of rear-end collisions between ADS and ADAS vehicles, and two separate models to analyze factors influencing crash types within each vehicle category. The results indicate that rear-end collisions dominate both ADS- and ADAS-involved crashes, with a higher proportion observed in ADAS-involved incidents (52.4%) compared to ADS-involved ones (40%). For ADS-involved crashes, the likelihood of a rear-end collision was significantly influenced by the pre-crash movement of the crash partner, with the highest risk occurring when the partner was proceeding straight. Additionally, on streets, a larger speed gap ratio significantly increased the odds of a rear-end collision. For ADAS-involved crashes, the speed gap ratio was a significant predictor across all road types, with higher gaps increasing rear-end risk. Furthermore, ADAS-involved rear-end collisions were significantly more likely to occur on highways, freeways, or rural roads compared to streets or intersections. The findings suggest that while ADS vehicles are more prone to rear-end collisions when partners are proceeding straight, ADAS vehicles face higher overall rear-end risks, particularly on highways. The authors attribute the high rate of ADAS rear-end crashes to driver over-reliance on the system, leading to delayed intervention or unexpected hard braking. These insights highlight distinct behavioral patterns and risks associated with different levels of automation. The study concludes that these findings can inform the design of ADS and ADAS control algorithms, external human-machine interfaces, and training programs for human road users to enhance safety in mixed traffic environments.
Key finding
Rear-end collisions dominate both ADS- and ADAS-involved crashes, but the specific contributing factors differ, with ADS crashes heavily influenced by the crash partner's movement and ADAS crashes more strongly associated with road type and speed gaps.
Methodology
dataset
Sample size: 214
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 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 | skipped | — | — | — | 3 | 2026-06-04 |
| 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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Information type
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- Empirical Findings: crash risk outcomes
- Methodological Resource: dataset resource