Reliable trajectory prediction of preceding target vehicles (PTVs) is crucial for the planning and decision making of automated vehicles. However, the future trajectory is affected by the driver's intention and diverse driving styles, which can hardly be predicted precisely, especially when the vehicle performs a lane change maneuver. In this study, we propose a lane crossing and final points generation (CFPG) model-based trajectory prediction approach for PTVs, in which the key influence factors such as the driver's intention and the mixed driving style are included. Firstly, we build a maneuver and stage recognition model upon the long short term memory (LSTM) to infer the current maneuver of the preceding target vehicle. Furthermore, the approach predicts the lane crossing point using a physics-based model combining with a deep conditional generative model trained by a deep neural network. Moreover, a maneuver-based model is adopted to predict the final point according to the prediction interval. In order to avoid the possible cumulative error caused by iteratively generating trajectories in traditional methods, we use a curve fitting method to obtain the predicted trajectory. Lane changing data collected from naturalistic driving scenarios are used to verify the proposed approach, and the results suggest more accurate and reliable prediction trajectories compared with conventional methods.

中文翻译：

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基于车道交叉和考虑驾驶风格的终点生成模型的前行目标车辆轨迹预测


前方目标车辆（PTV）的可靠轨迹预测对于自动驾驶车辆的规划和决策至关重要。然而，未来的轨迹受到驾驶员意图和不同驾驶风格的影响，很难准确预测，特别是当车辆进行变道操作时。在本研究中，我们提出了一种基于车道交叉和最终点生成（CFPG）模型的 PTV 轨迹预测方法，其中包括驾驶员意图和混合驾驶风格等关键影响因素。首先，我们基于长短期记忆（LSTM）建立机动和阶段识别模型，以推断前方目标车辆的当前机动。此外，该方法使用基于物理的模型与深度神经网络训练的深度条件生成模型相结合来预测车道交叉点。此外，采用基于机动的模型根据预测间隔来预测最终点。为了避免传统方法中迭代生成轨迹可能带来的累积误差，我们采用曲线拟合的方法来获得预测轨迹。从自然驾驶场景中收集的变道数据用于验证所提出的方法，结果表明与传统方法相比，预测轨迹更加准确和可靠。