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Autonomous vehicle can calculate the movement track of surrounding vehicles according to the track prediction algorithm, and make response to reduce driving risk, while the traditional track prediction model will produce large errors in the case of long-term series prediction. To address this issue, this paper proposed a trajectory prediction model based on the Informer algorithm, and used the publicly available dataset NGSIM to conduct experimental analysison. Firstly, it filtered the original data by using symmetric exponential moving average method(sEMA), and added a joint normalization layer to the original Informer encoder to extract features from different vehicles, reducing the motion error between different vehicles, and improving the prediction accuracy by considering the speed information of the vehicle itself and the vehicle movement information of the surrounding environment. Finally, it got the vehicle trajectory distribution at the future time through the decoder. The results show that the trajectory prediction error of the model is less than 0.5 m. Through the analysis of MAE and MSE results of trajectory prediction, when the prediction time exceeds 0.3 s, the trajectory prediction effect of Informer model is obviously better than other algorithms, which verifies the effectiveness of the model and algorithm. [ABSTRACT FROM AUTHOR]

Real-time calculation of high-precision forward-backward pyramid LK optical flow on embedded computing platform has become the decisive factor of whether the algorithm can be used in realistic scenarios such as autonomous driving. This paper presented a feature extraction method based on mesh division and a new forward- backward constraint method. To address the load unbalance in the optical flow calculation process, this paper proposed a dynamic window-based pyramid model. It also reduced the calculation bit width to further improve the overall performance. Using the 720P videos in realistic scenarios, this method yields significant performance gained over the GPU optimized version of OpenCV by 4.1 times on the embedded GPU platform TX2 and achieved 30 fps performance. It helped the SLAM system increase the frame rate to 28 fps in real car scene . This method improves the accuracy of the pose and point cloud in realistic scenarios, meeting the real-time application requirements of vehicle scenarios. [ABSTRACT FROM AUTHOR]