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Assessing autonomous driving performance and environmental influencing factors using real-world operational trajectory data.

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Title: Assessing autonomous driving performance and environmental influencing factors using real-world operational trajectory data.
Authors: Yang H; Research Institute of Highway Ministry of Transport, Beijing, China., Yu Q; MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China; State Key Laboratory of Subtropical Building and Urban Science, Shenzhen, 518060, China. Electronic address: yuq@szu.edu.cn.
Source: Accident; analysis and prevention [Accid Anal Prev] 2026 Oct; Vol. 236, pp. 108644. Date of Electronic Publication: 2026 Jul 01.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Pergamon Press Country of Publication: England NLM ID: 1254476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2057 (Electronic) Linking ISSN: 00014575 NLM ISO Abbreviation: Accid Anal Prev Subsets: MEDLINE
Imprint Name(s): Publication: Oxford : Pergamon Press
Original Publication: [New York, Pergamon Press]
MeSH Terms: Automobile Driving*/statistics & numerical data , Automobiles* , Man-Machine Systems*, Humans ; Autonomous Robots ; China ; Beijing ; Environment ; Environment Design
Abstract: Autonomous vehicles (AVs) are rapidly entering commercial deployment, but large-scale empirical comparisons with human driving remain limited. Using naturalistic data from two demonstration zones in Beijing and Shanghai, this study analyzes 444 vehicles and over 100 million 1Hz trajectory records from multiple permitted AV operators. A hierarchical segmentation pipeline merges any sub-five-second AV/manual mode-indicator switch with adjacent segments to suppress transient flicker that would otherwise be misread as takeover events, yielding stable segments for multi-granularity comparison of longitudinal dynamics, ride comfort, and human-machine interaction. The two zones represent contrasting operational regimes: a congested Beijing demonstration zone and a higher-speed Shanghai commercial robotaxi corridor, allowing recurrent cross-regime patterns to be separated from regime-sensitive variation. Routine jerk levels are similar between AV and human driving, whereas AV jerk is 47.5% lower during active longitudinal maneuvers in Shanghai. After filtering near-stationary events, in-motion takeovers are classified into Smooth, Braking, and Accelerating types. Accelerating takeovers account for roughly one quarter of in-motion events in both cities, whereas Braking dominates Shanghai and Smooth transitions dominate Beijing. An exposure-adjusted Shapley additive explanations (SHAP) analysis with AV traffic volume as a fixed offset identifies distinct environmental associations: curvature and sinuosity rank highest for Braking, whereas intersection proximity ranks first for Accelerating. Total disengagement counts therefore conflate behaviorally distinct intervention types and city-specific operational variation, both visible only after decomposing events by post-takeover dynamics and stratifying by operating regime.
(Copyright © 2026 Elsevier Ltd. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Autonomous vehicle; Human-autonomous vehicle interaction; Naturalistic driving data; Shapley additive explanations; Stability and efficiency trade-off; Takeover classification
Entry Date(s): Date Created: 20260701 Date Completed: 20260814 Latest Revision: 20260814
Update Code: 20260815
DOI: 10.1016/j.aap.2026.108644
PMID: 42385522
Database: MEDLINE
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  Data: Assessing autonomous driving performance and environmental influencing factors using real-world operational trajectory data.
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  Data: <searchLink fieldCode="AU" term="%22Yang+H%22">Yang H</searchLink>; Research Institute of Highway Ministry of Transport, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Yu+Q%22">Yu Q</searchLink>; MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China; State Key Laboratory of Subtropical Building and Urban Science, Shenzhen, 518060, China. Electronic address: yuq@szu.edu.cn.
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  Data: Autonomous vehicles (AVs) are rapidly entering commercial deployment, but large-scale empirical comparisons with human driving remain limited. Using naturalistic data from two demonstration zones in Beijing and Shanghai, this study analyzes 444 vehicles and over 100 million 1Hz trajectory records from multiple permitted AV operators. A hierarchical segmentation pipeline merges any sub-five-second AV/manual mode-indicator switch with adjacent segments to suppress transient flicker that would otherwise be misread as takeover events, yielding stable segments for multi-granularity comparison of longitudinal dynamics, ride comfort, and human-machine interaction. The two zones represent contrasting operational regimes: a congested Beijing demonstration zone and a higher-speed Shanghai commercial robotaxi corridor, allowing recurrent cross-regime patterns to be separated from regime-sensitive variation. Routine jerk levels are similar between AV and human driving, whereas AV jerk is 47.5% lower during active longitudinal maneuvers in Shanghai. After filtering near-stationary events, in-motion takeovers are classified into Smooth, Braking, and Accelerating types. Accelerating takeovers account for roughly one quarter of in-motion events in both cities, whereas Braking dominates Shanghai and Smooth transitions dominate Beijing. An exposure-adjusted Shapley additive explanations (SHAP) analysis with AV traffic volume as a fixed offset identifies distinct environmental associations: curvature and sinuosity rank highest for Braking, whereas intersection proximity ranks first for Accelerating. Total disengagement counts therefore conflate behaviorally distinct intervention types and city-specific operational variation, both visible only after decomposing events by post-takeover dynamics and stratifying by operating regime.<br /> (Copyright © 2026 Elsevier Ltd. All rights reserved.)
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