Zou Bin, Li Wenbo, Hou Xianjun, Tang Luqi, Yuan Quan
Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China.
Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel). 2022 Jun 25;22(13):4808. doi: 10.3390/s22134808.
Preceding vehicles have a significant impact on the safety of the vehicle, whether or not it has the same driving direction as an ego-vehicle. Reliable trajectory prediction of preceding vehicles is crucial for making safer planning. In this paper, we propose a framework for trajectory prediction of preceding target vehicles in an urban scenario using multi-sensor fusion. First, the preceding target vehicles historical trajectory is acquired using LIDAR, camera, and combined inertial navigation system fusion in the dynamic scene. Next, the Savitzky-Golay filter is taken to smooth the vehicle trajectory. Then, two transformer-based networks are built to predict preceding target vehicles' future trajectory, which are the traditional transformer and the cluster-based transformer. In a traditional transformer, preceding target vehicles trajectories are predicted using velocities in the X-axis and Y-axis. In the cluster-based transformer, the k-means algorithm and transformer are combined to predict trajectory in a high-dimensional space based on classification. Driving data from the real-world environment in Wuhan, China, are collected to train and validate the proposed preceding target vehicles trajectory prediction algorithm in the experiments. The result of the performance analysis confirms that the proposed two transformers methods can effectively predict the trajectory using multi-sensor fusion and cluster-based transformer method can achieve better performance than the traditional transformer.
前车对车辆安全有重大影响，无论其行驶方向是否与本车相同。可靠的前车轨迹预测对于制定更安全的规划至关重要。在本文中，我们提出了一种在城市场景中使用多传感器融合进行前车轨迹预测的框架。首先，在动态场景中使用激光雷达、摄像头和组合惯性导航系统融合获取前车的历史轨迹。接下来，采用Savitzky-Golay滤波器对车辆轨迹进行平滑处理。然后，构建了两个基于Transformer的网络来预测前车的未来轨迹，即传统Transformer和基于聚类的Transformer。在传统Transformer中，利用前车在X轴和Y轴上的速度来预测轨迹。在基于聚类的Transformer中，将k均值算法和Transformer相结合，基于分类在高维空间中预测轨迹。在中国武汉的真实环境中收集驾驶数据，在实验中对所提出的前车轨迹预测算法进行训练和验证。性能分析结果证实，所提出的两种Transformer方法能够利用多传感器融合有效地预测轨迹，并且基于聚类的Transformer方法比传统Transformer具有更好的性能。
