A Study on Learning and Simulating Personalized Car-Following Driving Style
A Study on Learning and Simulating Personalized Car-Following Driving Style
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DOI：
10.1109/itsc55140.2022.9922548
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Shili Sheng;Erfan Pakdamanian;Kyungtae Han;Ziran Wang;Lu Feng
中科院分区：
文献类型：
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作者：
Shili Sheng;Erfan Pakdamanian;Kyungtae Han;Ziran Wang;Lu Feng
自动驾驶汽车正逐渐进入人们的日常生活，为用户提供舒适的驾驶体验。通用和用户不可知的自动化车辆适应不同用户的不同驾驶风格的能力有限。这种限制不仅影响用户的满意度，而且还引起安全问题。从用户演示中学习可以提供关于用户驾驶偏好的直接见解。然而，很难用有限的数据来了解驾驶员的偏好。在这项研究中，我们使用一个无模型的逆强化学习方法来研究驾驶员的特征，在汽车跟驰的情况下，从一个自然的驾驶数据集，并显示这种方法是能够代表用户的偏好与奖励函数。为了在有限数据的情况下预测驾驶员的驾驶风格，我们应用高斯混合模型并计算特定驾驶员与驾驶员聚类的相似度。通过部分可观测马尔可夫决策过程（POMDP）模型设计了一种个性化自适应巡航控制（P-ACC）系统。将奖励函数与模拟驾驶员驾驶风格的模型相结合，并对相对距离进行约束，以保证驾驶安全。在不超过10个跟车事件的情况下，驾驶风格预测的准确率达到85.7%。基于模型的实验驾驶轨迹表明，P-ACC系统可以提供个性化的驾驶体验。
Automated vehicles are gradually entering people's daily life to provide a comfortable driving experience for the users. The generic and user-agnostic automated vehicles have limited ability to accommodate the different driving styles of different users. This limitation not only impacts users' satisfaction but also causes safety concerns. Learning from user demonstrations can provide direct insights regarding users' driving preferences. However, it is difficult to understand a driver's preference with limited data. In this study, we use a model-free inverse reinforcement learning method to study drivers' characteristics in the car-following scenario from a naturalistic driving dataset, and show this method is capable of representing users' preferences with reward functions. In order to predict the driving styles for drivers with limited data, we apply Gaussian Mixture Models and compute the similarity of a specific driver to the clusters of drivers. We design a personalized adaptive cruise control (P-ACC) system through a partially observable Markov decision process (POMDP) model. The reward function with the model to mimic drivers' driving style is integrated, with a constraint on the relative distance to ensure driving safety. Prediction of the driving styles achieves 85.7% accuracy with the data of less than 10 car-following events. The model-based experimental driving trajectories demonstrate that the P-ACC system can provide a personalized driving experience.