| 曾超杨,刘平,罗浩轩,刘明杰,朴昌浩.融合信息熵不确定性与驾驶风格的行驶风险评测方法研究[J].电子测量与仪器学报,2026,40(6):189-200 |
| 融合信息熵不确定性与驾驶风格的行驶风险评测方法研究 |
| Research on a driving risk assessment method integrating informationentropy uncertainty and driving style |
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| DOI： |
| 中文关键词: 行驶风险 不确定性 信息熵 驾驶风格 风险评测 |
| 英文关键词:driving risk uncertainty information entropy driving style risk assessment |
| 基金项目:国家重点研发计划(2022YFE0101000)、重庆市自然科学基金(CSTB2022NSCQ-MSX0355)项目资助 |
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| Author | Institution |
| Zeng Chaoyang | College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China |
| Liu Ping | College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China |
| Luo Haoxuan | College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China |
| Liu Mingjie | College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China |
| Piao Changhao | College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China |
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| 中文摘要: |
| 针对传统风险评测方法多依赖确定性指标，难以对周车行为不确定性进行动态量化风险评测的不足，提出一种融合信息熵不确定性与驾驶风格的风险评测方法，通过信息熵量化车辆运动状态的不确定性，并将不同驾驶风格量化为不同风险值以评测车辆行驶风险。首先，提出了基于信息熵的车辆运动状态不确定性评估方法用于感知行车风险；然后，在车辆速度、加加速度以及车头时距特征下基于K-means聚类开展了多种车辆驾驶风格识别；同时，结合权重机制构建了驾驶风格与信息熵融合的行驶风险评测方法；最后，在拥堵道路与高速场景下进行测试，从真实车辆能耗数据入手分析风险评测与车辆能耗的相关性，并与人工势场法、确定性风险评测法和基于学习的风险评测法进行对比。测试结果表明，提出方法均能对不同事件进行风险检测，响应时间指标上较其他3种方法分别平均降低50.55%、86.35%和81.85%，计算时间显著低于人工势场法和基于学习的方法，显示出该方法在行驶风险评测上的有效性和应用价值。 |
| 英文摘要: |
| Traditional risk assessment methods mostly rely on deterministic indicators, making it hard to dynamically quantify risks from the uncertainty of surrounding vehicle behaviors. To address this limitation, this study proposes a risk assessment approach integrating information entropy-based uncertainty quantification and driving style. Specifically, information entropy quantifies the uncertainty of vehicle motion states, while different driving styles are converted into quantifiable risk values for driving risk evaluation. Firstly, an information entropy-based method assesses the uncertainty of vehicle motion states for real-time risk perception. Secondly, K-means clustering is applied to vehicle speed, jerk, and time headway to identify diverse driving styles. A weighted mechanism is then established to fuse driving style and information entropy for the proposed risk assessment model. Finally, experiments are conducted under congested and highway scenarios. The correlation between evaluated risks and actual vehicle energy consumption is analyzed, and comparisons are made with the artificial potential field method, deterministic risk assessment method, and learning-based risk assessment method. Results show that the proposed method reliably detects risks in various events. In terms of response time, it is reduced by an average of 50.55%, 86.35%, and 81.85% compared with the other three methods, respectively. Meanwhile, its computation time is significantly lower than that of the artificial potential field method and the learning-based risk method, demonstrating the effectiveness and application value of the proposed method in driving risk assessment. |
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