| 引用本文: | 张杨杨,张浩,甘涛,冷昶,黄承超,张立军.典型驾驶场景下接管绩效预测及特征分析.软件学报,2025,36(12):5423-5437 |
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| 典型驾驶场景下接管绩效预测及特征分析 |
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张杨杨1,2,3, 张浩4,3, 甘涛1,2,3, 冷昶4,3, 黄承超5,3, 张立军1,2,3
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1.基础软件与系统重点实验室 (中国科学院 软件研究所), 北京 100190;2.计算机科学国家重点实验室(中国科学院 软件研究所) , 北京 100190;3.中国科学院大学, 北京 100049;4.中国科学院 软件研究所 人机交互技术与智能信息处理实验室, 北京 100190;5.中科南京软件技术研究院 软件安全与可信实验室, 江苏 南京 211135
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| 摘要: |
| 随着自动驾驶技术的快速发展, 车辆控制权的接管问题成为研究的热点. 装载辅助驾驶系统的汽车并不能完全处理所有的驾驶场景, 当实际驾驶场景超出辅助系统的操作设计域时, 仍需要人类驾驶员控制汽车以安全完成驾驶任务. 接管绩效是衡量驾驶员接管表现的重要指标, 包括接管反应时间和接管质量两个方面. 接管反应时间是指系统发出接管请求后到驾驶员控制方向盘的时间开销, 接管反应时间长短不仅一定程度上反映了当前驾驶员的状态, 还对后续面对复杂场景进行操作也有一定影响. 接管质量是指驾驶员获得车辆控制权后手动驾驶车辆的质量. 基于CARLA驾驶模拟器, 构建6个典型驾驶场景下, 对车辆控制权接管过程进行仿真并通过多通道采集系统搜集了31名驾驶员的生理信号和眼动数据. 根据驾驶员的接管表现, 参考国际标准基于多个车辆数据提出包括驾驶员接管反应时间、最大横、纵向加速度、最小碰撞时间在内的更为客观的接管绩效评价标准. 综合驾驶员数据、车辆数据和场景数据, 研究利用深度神经网络(DNN)模型对接管绩效进行了预测, 并运用SHAP模型分析各特征的影响, 以提高模型的解释性和透明度. 实验结果表明, 所提出的DNN模型在接管绩效预测方面优于传统机器学习方法, 预测准确率达到92.2%, 且具备良好的泛化性. SHAP分析揭示了心率变异性、驾驶经验、最小安全距离等关键特征对预测结果的重要影响. 为自动驾驶系统的安全性优化和人机交互设计提供了理论和实证基础, 对提高自动驾驶技术中人车合作的效率和安全性具有重要意义. |
| 关键词: 自动驾驶 接管绩效 深度神经网络 可解释性 人机交互 |
| DOI：10.13328/j.cnki.jos.007408 |
| 分类号:TP311 |
| 基金项目:中国科学院基础研究青年团队计划 (CASYSBR-040) |
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| Takeover Performance Prediction and Characteristic Analysis Under Typical Driving Scenarios |
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ZHANG Yang-Yang1,2,3, ZHANG Hao4,3, GAN Tao1,2,3, LENG Chang4,3, HUANG Cheng-Chao5,3, ZHANG Li-Jun1,2,3
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1.Key Laboratory of Systems Software (Institute of Software, Chinese Academy of Sciences), Beijing 100190, China;2.State Key Laboratory of Computer Science (Institute of Software, Chinese Academy of Sciences), Beijing 100190, China;3.University of Chinese Academy of Sciences, Beijing 100049, China;4.Laboratory of Human-computer Interaction Technology and Intelligent Information Processing, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;5.Software Security and Trustworthiness Laboratory, Nanjing Institute of Software Technology, Nanjing 211135, China
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| Abstract: |
| With the rapid development of autonomous driving technology, the issue of vehicle control takeover has become a prominent research topic. A car equipped with an assisted driving system cannot fully handle all driving scenarios. When the actual driving scenario exceeds the operational design domain of the assisted system, human intervention is still required to control the vehicle and ensure the safe completion of the driving task. Takeover performance is an extremely important metric for evaluating a driver’s performance during the takeover process, which includes takeover reaction time and takeover quality. The takeover reaction time refers to the time from the system’s takeover request to the driver’s control of the steering wheel. The length of the takeover response time not only reflects the driver’s current state but also affects the subsequent handling of complex scenarios. Takeover quality refers to the quality of manual vehicle operation by the driver after regaining control. This study, based on the CARLA driving simulator, constructs 6 typical driving scenarios, simulates the vehicle control takeover process, and collects physiological signals and eye movement data from 31 drivers using a multi-channel acquisition system. Based on the driver’s takeover performance, and regarding International standards, an objective takeover performance evaluation metric is proposed, incorporating the driver’s takeover reaction time, maximum horizontal and vertical accelerations, and minimum collision time, derived from multiple vehicle data. By combining driver data, vehicle data, and scenario data, a deep neural network (DNN) model predicts takeover performance, while the SHAP model analyzes the impact of each feature, improving the model’s interpretability and transparency. The experimental results show that the proposed DNN model outperforms traditional machine learning methods in predicting takeover performance, achieving an accuracy of 92.2% and demonstrating good generalization. The SHAP analysis reveals the impact of key features such as heart rate variability, driving experience, and minimum safe distance on the prediction results. This research provides a theoretical and empirical foundation for the safety optimization and human-computer interaction design of autonomous driving systems and is of great significance for improving the efficiency and safety of human-vehicle cooperation in autonomous driving technology. |
| Key words: autonomous driving takeover performance deep neural network (DNN) interpretability human-machine interface |
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