This article presents a method for the trajectory prediction of surrounding vehicles and proactive longitudinal control of autonomous vehicles (AVs) in an urban road environment. A long short-term memory (LSTM)-based deep learning model is designed for the surrounding vehicles’ trajectory prediction. In our model, the historical evolution of the relation between a target vehicle and lanes is considered to learn the driver’s behavior in a lane-aware manner. Interaction among adjacent vehicles is captured based on a graph convolutional network (GCN), which uses a self-attention mechanism. Compared to other approaches, our prediction model utilizes environment information that is acquirable in AVs with local sensors. A model predictive control (MPC) is designed to derive the control inputs of acceleration for AVs. The proposed control method utilizes the prediction results of the target vehicle to give action requests to AV in a proactive manner considering both safety and ride quality. The results of comparative studies indicate that the proposed prediction model achieves improved accuracy compared to baselines. The control results provided by automated driving tests show that the proposed control algorithm applied by the LSTM-based prediction model enables AVs to achieve safety with respect to surrounding vehicles and provide ride comfort to passengers.

中文翻译：

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使用基于图的深度学习进行自动驾驶汽车纵向控制的轨迹预测：城市动态交通环境中自动驾驶的主动方法

本文介绍了一种在城市道路环境中预测周围车辆轨迹和主动纵向控制自动驾驶车辆 (AV) 的方法。设计了一种基于长短期记忆 (LSTM) 的深度学习模型来预测周围车辆的轨迹。在我们的模型中，目标车辆与车道之间关系的历史演化被认为是以车道感知的方式学习驾驶员的行为。基于使用自我注意机制的图卷积网络 (GCN) 捕获相邻车辆之间的交互。与其他方法相比，我们的预测模型利用了可在带有本地传感器的 AV 中获取的环境信息。模型预测控制 (MPC) 旨在推导 AV 的加速度控制输入。所提出的控制方法利用目标车辆的预测结果以主动的方式向 AV 发出动作请求，同时考虑到安全性和乘坐质量。比较研究的结果表明，与基线相比，所提出的预测模型实现了更高的准确性。自动驾驶测试提供的控制结果表明，基于 LSTM 的预测模型应用所提出的控制算法使 AV 能够实现相对于周围车辆的安全并为乘客提供乘坐舒适性。