驾驶人的接管绩效对于有条件自动驾驶车辆的安全性、驾乘体验与接受度具有重要意义。为了探究驾驶人行为对接管绩效的影响机理,提出1个代表接管绩效的综合评价表征指标——接管绩效水平(takeover performance level,TOPL),并构建基于改...驾驶人的接管绩效对于有条件自动驾驶车辆的安全性、驾乘体验与接受度具有重要意义。为了探究驾驶人行为对接管绩效的影响机理,提出1个代表接管绩效的综合评价表征指标——接管绩效水平(takeover performance level,TOPL),并构建基于改进EWM-TOPSIS的评估模型。该模型通过熵权法(entropy weight method,EWM)确定各指标客观权重,利用基于最优解偏好排序技术(technique for order preference by similarity to ideal solution,TOPSIS)模型中正负理想解对各指标进行编码映射。为了验证模型的有效性,招募46名驾驶人参与人机共驾接管实验,并提取表征驾驶人接管绩效安全性、舒适性和平稳性的多维度评价指标,深入探讨驾驶人在接管过程中执行的非驾驶相关任务,以及接管时间预算对驾驶人接管绩效水平的影响机理。进一步地,分析驾驶人年龄和标准型非驾驶相关任务完成绩效对TOPL的影响机理。研究结果表明:驾驶人年龄,非驾驶相关任务完成绩效对TOPL有显著影响,处于5万~10万km和0~5万km,10万~100万km之间的驾驶里程存在显著差异。车辆的最大横摆角速率、最大横向加速度,最大横向速度,油门深度标准差与TOPL之间存在显著的负相关关系,而接管边界时距与TOPL呈现显著的正相关关系。针对不同接管时间预算与非驾驶相关任务完成绩效,TOPL在接管时间预算为4 s时最低,接管程度紧急下的接管绩效水平较低。当驾驶人完成非驾驶相关任务低于60分时,TOPL最高,并且随着分数的增大TOPL有下降趋势。展开更多
In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle's safety and ride comfort. I...In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle's safety and ride comfort. In the U.S state of California, the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing. On 1 January 2016,seven manufacturers submitted their first disengagement reports:Bosch, Delphi, Google, Nissan, Mercedes-Benz, Volkswagen, and Tesla Motors. This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers(SAE) Level 2 and Level 3 automation technologies.Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail. The mechanisms and time taken for take-over transition occurred in disengagements are studied. Finally, recommendations for OEMs, manufacturers, and government organizations are also discussed.展开更多
文摘驾驶人的接管绩效对于有条件自动驾驶车辆的安全性、驾乘体验与接受度具有重要意义。为了探究驾驶人行为对接管绩效的影响机理,提出1个代表接管绩效的综合评价表征指标——接管绩效水平(takeover performance level,TOPL),并构建基于改进EWM-TOPSIS的评估模型。该模型通过熵权法(entropy weight method,EWM)确定各指标客观权重,利用基于最优解偏好排序技术(technique for order preference by similarity to ideal solution,TOPSIS)模型中正负理想解对各指标进行编码映射。为了验证模型的有效性,招募46名驾驶人参与人机共驾接管实验,并提取表征驾驶人接管绩效安全性、舒适性和平稳性的多维度评价指标,深入探讨驾驶人在接管过程中执行的非驾驶相关任务,以及接管时间预算对驾驶人接管绩效水平的影响机理。进一步地,分析驾驶人年龄和标准型非驾驶相关任务完成绩效对TOPL的影响机理。研究结果表明:驾驶人年龄,非驾驶相关任务完成绩效对TOPL有显著影响,处于5万~10万km和0~5万km,10万~100万km之间的驾驶里程存在显著差异。车辆的最大横摆角速率、最大横向加速度,最大横向速度,油门深度标准差与TOPL之间存在显著的负相关关系,而接管边界时距与TOPL呈现显著的正相关关系。针对不同接管时间预算与非驾驶相关任务完成绩效,TOPL在接管时间预算为4 s时最低,接管程度紧急下的接管绩效水平较低。当驾驶人完成非驾驶相关任务低于60分时,TOPL最高,并且随着分数的增大TOPL有下降趋势。
基金supported by Jaguar Land Roverthe UK-EPSRC grant EP/N012089/1 as part of the jointly funded Towards Autonomy:Smart and Connected Control(TASCC)Programme
文摘In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle's safety and ride comfort. In the U.S state of California, the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing. On 1 January 2016,seven manufacturers submitted their first disengagement reports:Bosch, Delphi, Google, Nissan, Mercedes-Benz, Volkswagen, and Tesla Motors. This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers(SAE) Level 2 and Level 3 automation technologies.Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail. The mechanisms and time taken for take-over transition occurred in disengagements are studied. Finally, recommendations for OEMs, manufacturers, and government organizations are also discussed.
