In this article, a novel approach of decision-making and motion control is designed for realizing safe and personalized driving of autonomous vehicles. A new lane-change intention generation model and a new lane-change decision-making algorithm are proposed. The feature of the proposed decision-making module is that the interactions between the ego vehicle and other surrounding vehicles are represented by the dynamic potential field (DPF) and embedded in the gap acceptance model to ensure the safety and personalization during driving. In addition, an integrated trajectory planning and tracking control algorithm, which incorporates the artificial potential field and constrained Delaunay triangulation (CDT) into the model predictive control framework, is developed. The newly developed integrated controller allows efficient execution of the expected motion. The proposed approach is tested under different driving conditions and further compared with an existing baseline method. The results show that the proposed approach is able to make safe and personalized decisions, and execute motion control more efficiently for automated driving under dynamic situations, validating its feasibility and effectiveness.

中文翻译：

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迈向安全和个性化的自动驾驶：采用DPF和CDT技术的决策和运动控制

在本文中，设计了一种新颖的决策和运动控制方法，以实现自动驾驶汽车的安全性和个性化驾驶。提出了一种新的变道意图生成模型和新的变道决策算法。所提出的决策模块的特征在于，自我车辆与其他周围车辆之间的交互作用由动态势场（DPF）表示，并嵌入在间隙接受模型中，以确保行驶过程中的安全性和个性化。此外，开发了一种集成的轨迹规划和跟踪控制算法，该算法将人工势场和约束的Delaunay三角剖分（CDT）纳入了模型预测控制框架。新开发的集成控制器允许有效执行预期的运动。所提出的方法在不同的驾驶条件下进行了测试，并与现有的基准方法进行了进一步的比较。结果表明，所提出的方法能够做出安全和个性化的决策，并能在动态情况下更有效地执行运动控制以实现自动驾驶，从而验证了其可行性和有效性。