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Study on Occupants’ Perceived Risk in Overtaking Scenarios
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Junyi CHEN1, Mengqi TU1, Xingyu XING2, Peiyi WANG1, Xingyu ZHAO1, Xiaoyi WANG2, Xiang YIN3, Haolan MENG1
Chinese Journal of Automotive Engineering | 2025, 15(4) : 516 - 527
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Chinese Journal of Automotive Engineering | 2025, 15(4): 516-527
Intelligent & Connected Technologies Section/Editor in Chief:GAO Zhenhai
Study on Occupants’ Perceived Risk in Overtaking Scenarios
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Junyi CHEN1, Mengqi TU1, Xingyu XING2, Peiyi WANG1, Xingyu ZHAO1, Xiaoyi WANG2, Xiang YIN3, Haolan MENG1
Affiliations
  • 1 School of Automotive Studies,Tongji University,Shanghai 201804,China
  • 2 Shanghai Motor Vehicle Inspection Certification & Tech Innovation Center Co.,Ltd.,Shanghai 201805,China
  • 3 SAIC Motor R&D Innovation Headquarters,Shanghai 201804,China
Published: 2025-07-20 doi: 10.3969/j.issn.2095‒1469.2025.04.09
Outline
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When an autonomous vehicle (AV) is in motion, the driving risks caused by the external environment can lead to occupants' distrust, reducing their acceptance of AVs. Therefore, quantifying occupants' perceived risk is crucial for designing and evaluating AV behavior, as it provides theoretical support for mitigating that risk. The paper quantifies the relationship between objective scenario-level risk factors and subjective perception in overtaking scenarios using a logistic regression model. Firstly, based on 92 overtaking segments of data collected in real-world driving experiments, 7 candidate risk factors are identified. Then, a logistic regression model is established in which the 5 risk factors that passed the hypothesis test are used as the independent variables and the binary classification of occupants' perceived risk serves as the dependent variable. The model analysis indicates that three factors, i.e. risk in adjacent areas ( S), time to collision ( t c o l) and time headway ( t h e a d), are significantly related to occupants' perceived risk, with t h e a d being the most influential factor. To classify whether occupants perceive risk, the cut-off value of the prediction model is set at 0.462, which is calculated from the Receiver Operating Characteristic (ROC) curve. By using the HighD dataset, the cut-off value is verified and the accuracy of the prediction model is found to be 89.1%. On this basis, three optimized driving strategies are formulated to mitigate high perceived risk in overtaking scenarios. These three strategies are compared in driving-simulator tests in terms of traffic efficiency and perceived risk, confirming the validity of the model's analysis conclusions.

autonomous vehicles  /  perceived risk  /  scenario risk factors  /  binary logistic regression
Junyi CHEN, Mengqi TU, Xingyu XING, Peiyi WANG, Xingyu ZHAO, Xiaoyi WANG, Xiang YIN, Haolan MENG. Study on Occupants’ Perceived Risk in Overtaking Scenarios[J]. Chinese Journal of Automotive Engineering, 2025 , 15 (4) : 516 -527 . DOI: 10.3969/j.issn.2095‒1469.2025.04.09
Year 2025 volume 15 Issue 4
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Article Info
doi: 10.3969/j.issn.2095‒1469.2025.04.09
  • Receive Date:2025-02-14
  • Online Date:2025-09-10
  • Published:2025-07-20
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History
  • Received:2025-02-14
  • Revised:2025-04-11
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Affiliations
    1 School of Automotive Studies,Tongji University,Shanghai 201804,China
    2 Shanghai Motor Vehicle Inspection Certification & Tech Innovation Center Co.,Ltd.,Shanghai 201805,China
    3 SAIC Motor R&D Innovation Headquarters,Shanghai 201804,China
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表12种不同金属材料的力学参数

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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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