Cooperative Driving in Mixed Traffic of Manned and Unmanned Vehicles based on Human Driving Behavior Understanding
Cooperative Driving in Mixed Traffic of Manned and Unmanned Vehicles based on Human Driving Behavior Understanding
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DOI：
10.1109/icra48891.2023.10160282
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Jiaxing Lu;Sanzida Hossain;W. Sheng;H. Bai
中科院分区：
文献类型：
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作者：
Jiaxing Lu;Sanzida Hossain;W. Sheng;H. Bai
为了实现人车与无人车混合交通中的安全协同驾驶，有必要对人类驾驶员的驾驶行为进行理解和建模。提出了一种基于隐马尔可夫模型(HMM)的驾驶员控制和车辆动力学分析方法，对驾驶员的加速、制动、变道等行为进行识别。在已知驾驶员行为的基础上，利用概率模型对驾驶员驾驶车辆的加速度进行预测。这种关于驾驶员行为和车辆行为的信息可以用于实现更安全的协作驾驶，这是通过车对车(V2V)通信和模型预测控制(MPC)实现的。该方法在我们定制的协同驾驶试验台上进行了测试和评估。实验结果表明，上述驾驶员行为模型是有效和准确的。通过一个车道合并场景的初步案例研究，进一步验证了该算法的有效性和能力。
To achieve safe cooperative driving in mixed traffic of manned and unmanned vehicles, it is necessary to understand and model human drivers' driving behaviors. This paper proposed a Hidden Markov Model (HMM)-based method to analyze human driver's control and vehicle's dynamics; and then recognize the human driver's action, such as accelerating, braking, and changing lanes. With the knowledge of the human driver's actions, a probability model is used to predict the human-driven vehicle's acceleration. Such information on the driver behavior and the vehicle behavior can be used to achieve safer cooperative driving, which is realized using vehicle-to-vehicle (V2V) communication and model predictive control (MPC). The proposed method was tested and evaluated in our custom-built cooperative driving testbed. Experimental results show that the above driver action model is effective and accurate. A preliminary case study on a lane merging scenario is provided to further validate its effectiveness and capability.