A hierarchical framework for improving ride comfort of autonomous vehicles via deep reinforcement learning with external knowledge

Ride comfort plays an important role in determining the public acceptance of autonomous vehicles (AVs). Many factors, such as road profile, driving speed, and suspension system, influence the ride comfort of AVs. This study proposes a hierarchical framework for improving ride comfort by integrating speed planning and suspension control in a vehicle-to-everything environment. Based on safe, comfortable, and efficient speed planning via dynamic programming, a deep reinforcement learning-based suspension control is proposed to adapt to the changing pavement conditions. Specifically, a deep deterministic policy gradient with external knowledge (EK-DDPG) algorithm is designed for the efficient self-adaptation of suspension control strategies. The external knowledge of action selection and value estimation from other AVs are combined into the loss functions of the DDPG algorithm. In numerical experiments, real-world pavements detected in 11 districts of Shanghai, China, are applied to verify the proposed method. Experimental results demonstrate that the EK-DDPG-based suspension control improves ride comfort on untrained rough pavements by 27.95% and 3.32%, compared to a model predictive control (MPC) baseline and a DDPG baseline, respectively. Meanwhile, the EK-DDPG-based suspension control improves computational efficiency by 22.97%, compared to the MPC baseline, and performs at the same level as the DDPD baseline. This study provides a generalized and computationally efficient approach for improving the ride comfort of AVs.     

A three-stage multi-objective heterogeneous integrated model with decomposition-reconstruction mechanism and adaptive segmentation error correction method for ship motion multi-step prediction

Reliable and accurate ship motion prediction is essential for ship navigation at sea and marine operations. Although previous studies have yielded rich results in the field of ship motion prediction, most of them have ignored the importance of the dynamic characteristics of ship motion for constructing forecasting models. Besides, the limitations of the single model and the autocorrelation characteristics of the residual series are also unfavorable factors that hinder the forecasting performance. To fill these gaps, a multi-objective heterogeneous integration model based on decomposition-reconstruction mechanism and adaptive segmentation error correction method is proposed in this paper for ship motion multi-step prediction. Specifically, the proposed model is divided into three stages, which are decomposition-reconstruction mechanism, multi-objective heterogeneous integration model and adaptive segmentation error correction method. The effectiveness of the proposed model is verified using four sets of real ship motion data collected from two sites in the South China Sea. The evaluation results show that the proposed model can effectively improve the prediction performance and outperforms other traditional models and state-of-the-art models in the field of ship motion prediction. Prospectively, the model proposed in this study can be used as an effective aid to ship warning systems and has the potential for practical application in ship marine operations.     

高速路网全时安全运行风险智能管控的内涵与架构

基于风险管控的基本理论和“零死亡愿景”,提出高速路网全时安全运行风险智能管控的要素、关系及内涵。分析管控系统的需求,确定全息运行数据采集与处理、实时交通风险状态诊断与评估、交通风险状态预测、决策支持、查询功能和精细化展示等六大总体功能。基于系统工程理论分析,建立面向全时安全运行风险管控的现代局域高速路网信息模型。采用数据流的基本表达方法,建立基于“云-雾-端”融合与“三自”一体的高速路网全时安全运行风险智能管控总体数据流图。本研究提出的全时安全内涵及其架构可为高速路网智能管控提供理论支撑,对明确高速公路交通安全领域下一步的研究与实践具有参考意义。