Volume 41 Issue 5
Oct.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(5): 35-42
PANG Shaorong, ZHANG Shibo, LUO Longhao, LUO Yong, LI Min. A Method for Evaluating Safety of Driving Scenes with Intelligent Connected Vehicles Based on an Improved Cloud Combination Weighting[J]. Journal of Transport Information and Safety, 2023, 41(5): 35-42. doi: 10.3963/j.jssn.1674-4861.2023.05.004
Citation: PANG Shaorong, ZHANG Shibo, LUO Longhao, LUO Yong, LI Min. A Method for Evaluating Safety of Driving Scenes with Intelligent Connected Vehicles Based on an Improved Cloud Combination Weighting[J]. Journal of Transport Information and Safety, 2023, 41(5): 35-42. doi: 10.3963/j.jssn.1674-4861.2023.05.004
shu

A Method for Evaluating Safety of Driving Scenes with Intelligent Connected Vehicles Based on an Improved Cloud Combination Weighting

doi: 10.3963/j.jssn.1674-4861.2023.05.004
  • 1.

    School of Automobile and Transportation, Xihua University, Chengdu 610039, China

  • 2.

    Sichuan Xihua Jiaotong Forensic Center, Chengdu 610039, China

  • 3.

    Department of Transportation and Economic Management, Sichuan Vocational and Technical College of Communications, Chengdu 611130, China

  • Received Date: 2022-06-13
    Available Online: 2024-01-18
    Abstract
  • Accurate and reliable safety evaluation of driving scenarios is the basis for promotion and application of intelligent connected vehicles. However, fuzziness and randomness brought by complex and changeable driving scenarios cannot be fully considered by evaluation methods based on fixed weighting. A safety evaluation method for driving scenarios of intelligent connected vehicles based on improved cloud combination weighting is proposed. The driving scenarios element database of intelligent connected vehicles is established, which includes static environment, dynamic behavior, intelligent element layers. A scenarios design scheme is developed. The scenarios are deconstructed into functional scenarios, logical scenarios, and specific scenarios. Each scenario is carefully designed with relevant elements. The concept of cloud model and the game theory are combined to improve cloud combination weighting. A comprehensive cloud is constructed based on the cloud model algorithm to characterize the security of each scenario, and an ideal cloud evaluation model is established. The relative similarity index is put forward as an evaluation result, enabling quantitative analysis and ranking of scenario safety. The reliability of this method is verified by comparing with the analytic hierarchy process (AHP), superiority chart, entropy method, variation coefficient method, game combination weighting, and normal cloud combination weighting. The Pearson correlation coefficient between evaluation and simulation results is 0.649, significantly correlated at the 99% confidence level. It is 5.5%, 7.8%, 19.7%, 13.7%, 8.1%, and 0.8% higher than the above evaluation methods, respectively. In the simulation test, the accuracy of accident identification of the proposed method is 78.13%, higher than the 44.29% used by Baumann et al, and 57.2% used by Xia et al. The result shows advantages of subjective and objective weighting evaluation methods. Inauthentic evaluation results caused by the current fixed numerical weight are improved, and the accuracy of relevant evaluation is increased.

     

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