Exploring Factors Influencing Crash Occurrence Involving Autonomous Vehicle: Random Parameters Model With Heterogeneity in Means and Variances Approach
Autonomous vehicle (AV) technologies are expected to play a crucial role in reducing traffic crashes occurred by human factors; however, foreseeable coexistence of AVs with human-driven vehicles (HDVs) for an extended transition period raises safety concerns. Understanding factors influencing AV-inv...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10850905/ |
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| author | Haocheng Li Amjad Pervez Jaeyoung Jay Lee |
| author_facet | Haocheng Li Amjad Pervez Jaeyoung Jay Lee |
| author_sort | Haocheng Li |
| collection | DOAJ |
| description | Autonomous vehicle (AV) technologies are expected to play a crucial role in reducing traffic crashes occurred by human factors; however, foreseeable coexistence of AVs with human-driven vehicles (HDVs) for an extended transition period raises safety concerns. Understanding factors influencing AV-involved crashes is crucial, especially as human drivers may struggle to comprehend the behavior of AVs during interactions. This study addresses this gap by employing a random parameter probit model with heterogeneity in means and variances. Dataset comprises AV crash records obtained from the California Department of Motor Vehicles from 2018 to the first quarter of 2024. Crashes on roadway segments and intersection are modeled separately. Modeling results reveal that factors such as poor lighting conditions, braking maneuver of AVs, proceeding straight movement of HDVs, involvement of bikes/scooters, residential land-use significantly contribute to AV-involved crash occurrence on segments and at intersections. On segments, first quarter of the year, the retail/entertainment land use, sideswipe collision, dangerous maneuver of HDVs and proceeding straight moment of AVs affect the likelihood of AV-involved crashes. Meanwhile, at intersection, rear-end collision, raining/snowing, unusual road conditions, four-leg intersection, lack of pedestrian island/intersection control significantly increases the probability of AV-involved crashes while angle collision and large skew angle decreases it. The findings highlight the need for more targeted goals to improve AV’s safety, such as enhancing AV sensor perception capabilities, incorporating scenario-based tests by categorization of crash location, and developing mass education initiatives to facilitate the broader acceptance and understanding of AV technologies. |
| format | Article |
| id | doaj-art-738a5eddf2ab427cbe7f32604c0b51f7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-738a5eddf2ab427cbe7f32604c0b51f72025-01-31T00:01:05ZengIEEEIEEE Access2169-35362025-01-0113168791689510.1109/ACCESS.2025.353293710850905Exploring Factors Influencing Crash Occurrence Involving Autonomous Vehicle: Random Parameters Model With Heterogeneity in Means and Variances ApproachHaocheng Li0https://orcid.org/0009-0007-7410-4637Amjad Pervez1https://orcid.org/0000-0001-6283-2871Jaeyoung Jay Lee2https://orcid.org/0000-0003-1211-688XSchool of Traffic and Transportation Engineering, Central South University, Changsha, ChinaSchool of Traffic and Transportation Engineering, Central South University, Changsha, ChinaSchool of Traffic and Transportation Engineering, Central South University, Changsha, ChinaAutonomous vehicle (AV) technologies are expected to play a crucial role in reducing traffic crashes occurred by human factors; however, foreseeable coexistence of AVs with human-driven vehicles (HDVs) for an extended transition period raises safety concerns. Understanding factors influencing AV-involved crashes is crucial, especially as human drivers may struggle to comprehend the behavior of AVs during interactions. This study addresses this gap by employing a random parameter probit model with heterogeneity in means and variances. Dataset comprises AV crash records obtained from the California Department of Motor Vehicles from 2018 to the first quarter of 2024. Crashes on roadway segments and intersection are modeled separately. Modeling results reveal that factors such as poor lighting conditions, braking maneuver of AVs, proceeding straight movement of HDVs, involvement of bikes/scooters, residential land-use significantly contribute to AV-involved crash occurrence on segments and at intersections. On segments, first quarter of the year, the retail/entertainment land use, sideswipe collision, dangerous maneuver of HDVs and proceeding straight moment of AVs affect the likelihood of AV-involved crashes. Meanwhile, at intersection, rear-end collision, raining/snowing, unusual road conditions, four-leg intersection, lack of pedestrian island/intersection control significantly increases the probability of AV-involved crashes while angle collision and large skew angle decreases it. The findings highlight the need for more targeted goals to improve AV’s safety, such as enhancing AV sensor perception capabilities, incorporating scenario-based tests by categorization of crash location, and developing mass education initiatives to facilitate the broader acceptance and understanding of AV technologies.https://ieeexplore.ieee.org/document/10850905/Autonomous vehiclecrash analysisrandom parameters with heterogeneity in means and variancesroadway segmentintersectionroad safety |
| spellingShingle | Haocheng Li Amjad Pervez Jaeyoung Jay Lee Exploring Factors Influencing Crash Occurrence Involving Autonomous Vehicle: Random Parameters Model With Heterogeneity in Means and Variances Approach IEEE Access Autonomous vehicle crash analysis random parameters with heterogeneity in means and variances roadway segment intersection road safety |
| title | Exploring Factors Influencing Crash Occurrence Involving Autonomous Vehicle: Random Parameters Model With Heterogeneity in Means and Variances Approach |
| title_full | Exploring Factors Influencing Crash Occurrence Involving Autonomous Vehicle: Random Parameters Model With Heterogeneity in Means and Variances Approach |
| title_fullStr | Exploring Factors Influencing Crash Occurrence Involving Autonomous Vehicle: Random Parameters Model With Heterogeneity in Means and Variances Approach |
| title_full_unstemmed | Exploring Factors Influencing Crash Occurrence Involving Autonomous Vehicle: Random Parameters Model With Heterogeneity in Means and Variances Approach |
| title_short | Exploring Factors Influencing Crash Occurrence Involving Autonomous Vehicle: Random Parameters Model With Heterogeneity in Means and Variances Approach |
| title_sort | exploring factors influencing crash occurrence involving autonomous vehicle random parameters model with heterogeneity in means and variances approach |
| topic | Autonomous vehicle crash analysis random parameters with heterogeneity in means and variances roadway segment intersection road safety |
| url | https://ieeexplore.ieee.org/document/10850905/ |
| work_keys_str_mv | AT haochengli exploringfactorsinfluencingcrashoccurrenceinvolvingautonomousvehiclerandomparametersmodelwithheterogeneityinmeansandvariancesapproach AT amjadpervez exploringfactorsinfluencingcrashoccurrenceinvolvingautonomousvehiclerandomparametersmodelwithheterogeneityinmeansandvariancesapproach AT jaeyoungjaylee exploringfactorsinfluencingcrashoccurrenceinvolvingautonomousvehiclerandomparametersmodelwithheterogeneityinmeansandvariancesapproach |