Prediction of Personalized Driving Behaviors via Driver-adaptive Deep Generative Models
Prediction of Personalized Driving Behaviors via Driver-adaptive Deep Generative Models
复制标题
通过驾驶员自适应深度生成模型预测个性化驾驶行为
DOI：
10.1109/iv48863.2021.9575671
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
and Kazuya Takeda
中科院分区：
文献类型：
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作者：
Naren Bao;Alexander Carballo;and Kazuya Takeda
人类驾驶员具有复杂而独特的驾驶特征，即使在常见的、定义明确的场景（如变道）中驾驶也是如此。在这项研究中，我们建议使用概率，深度生成模型来预测个性化的驾驶行为，包括速度，加速度和转向角度序列。概率方法被应用到模型中的不确定性，在驾驶行为的个体驾驶员作为一个分布，而周围的车辆和驾驶员ID信息被认为是给定的条件下的分布。我们使用真实世界的驾驶数据训练个体驾驶员模型，并使用它们来预测动态环境中未来驾驶行为的序列，使用历史数据来考虑个人驾驶风格。我们的研究结果表明，所提出的驾驶员行为建模方法是能够学习从驾驶员的车辆操作数据和他们与周围的车辆的相互作用，以再现其特定的驾驶风格。
Human drivers have complex and unique driving characteristics, even when driving in common, well-defined scenarios such as lane changes. In this study, we propose using probabilistic, deep generative models to predict personalized driving behavior including velocity, acceleration, and steering angle sequence. Probabilistic approaches are applied to model uncertainty in the driving behavior of individual drivers as a distribution, while surrounding vehicle and driver ID information are considered as given conditions in the distribution. We train individual driver models using real-world driving data, and use them to predict sequences of future driving behavior in dynamic environments, using historical data to take personal driving styles into account. Our results show that the proposed driver behavior modeling method is able to learn from a driver's vehicle operation data and their interactions with surrounding vehicles to reproduce their specific driving style.