| 引用本文: | 马艳丽,董方琦,秦钦,郭蓥蓥.基于行车风险场的自动驾驶接管风险评估模型[J].哈尔滨工业大学学报,2024,56(9):106.DOI:10.11918/202211073 |
| MA Yanli,DONG Fangqi,QIN Qin,GUO Yingying.Risk evaluation model of autonomous driving takeover based on driving risk field[J].Journal of Harbin Institute of Technology,2024,56(9):106.DOI:10.11918/202211073 |
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| 摘要: |
| 为评估L3级自动驾驶的接管风险,降低接管过程中的事故率,设计城市快速路接管场景并开展驾驶模拟实验,以行车风险场理论为基础,采用动态和静态风险分布函数反映其他交通单元对接管风险的影响,引入车辆性能概率因子对自动驾驶接管过程中的事故发生概率进行表征,同时考虑接管反应时间的影响,构建自动驾驶接管风险评估模型,基于驾驶模拟实验获取接管反应时间数据和车辆轨迹数据对模型参数进行标定,并与碰撞时间倒数进行对比,验证模型的有效性。结果表明:驾驶员接管后1~9 s内模型计算所得的接管风险指数的变化趋势与碰撞时间倒数一致,但接管过程中风险指数的均方根误差均值（0.059）相较于碰撞时间倒数的均方根误差均值（0.093）下降了37%。构建的模型能够有效评估驾驶员的接管风险,且模型在表征风险的准确性方面优于碰撞时间倒数。 |
| 关键词: 交通工程 接管风险评估 行车风险场 轨迹数据 自动驾驶 驾驶模拟 |
| DOI：10.11918/202211073 |
| 分类号:U491 |
| 文献标识码:A |
| 基金项目:国家自然科学基金面上项目(52372325)；黑龙江省自然科学基金(LH2020E056) |
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| Risk evaluation model of autonomous driving takeover based on driving risk field |
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MA Yanli,DONG Fangqi,QIN Qin,GUO Yingying
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(School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China)
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| Abstract: |
| In the event that the L3 autonomous driving system fails or has difficulty handling complex traffic environments, the driver is required to takeover in an emergency, which can easily lead to traffic accidents. In order to assess the takeover risk of L3 autonomous vehicles, a takeover scenario on the urban expressway was designed and driving simulation experiments were carried out. Based on the theory of driving risk field, dynamic and static risk distribution functions were used to reflect the influence of other traffic units on the takeover risk of ego vehicle. And then, vehicle performance probability factor was introduced to indicate the probability of potential traffic accidents caused by abnormal vehicle trajectories during the takeover process, as well as considering the influence of takeover response time, a risk evaluation model of autonomous driving takeover was constructed. The model parameters were calibrated on the basis of the takeover reaction time and trajectory data obtained from the driving simulation experiments and compared with the inverse time-to-collision to verify the model. The results showed that the values of takeover risk index calculated by the model from 1 s to 9 s after the driver took over were consistent with the inverse time-to-collision. However, the root mean square error of risk index during the takeover (0.059) decreased by 37% compared to the root mean square error of the inverse time-to-collision (0.093). In summary, the constructed model can effectively assess the risk of driver takeover, and the model is more accurate than the inverse time-to-collision in describing the risk. |
| Key words: traffic engineering takeover risk evaluation driving risk field trajectory data autonomous driving driving simulation |
