Pedestrian Trajectory Prediction Method Based On 3D Point Cloud

A pedstrian trajectory prediction method is proposed to address the problems of poor accuracy in predicting pedestian motion trajectories and high deviation in trajectory prediction when pedestrians make significant turns in autonomous vehicles. The method uses a LiDAR and an improved trajectory prediction algorithm as the detection sensor. By processing continuous single frame point cloud data through streaming point cloud processing, the minimum bounding box of pedestrians is clustered and extracted, and its centroid is used as the positioning coordinate as the input for the prediction algorithm. At the same time, multiple motion models are established based on the motion characteristics of pedestrians, and an improved adaptive interactive multi model unscented Kalman filtering algorithm is used to predict pedestrian trajectories continuously. The difference between the real and predicted trajectories is used as a quantitative indicator to compare the improved algorithm with the traditional interactive multi model unscented Kalman filtering algorithm. The experimental results show that the adaptive interactive multi model unscented Kalman filtering algorithm reduces the overall prediction average error by 23.02% compared to the traditional interactive multi model unscented Kalman filtering algorithm and reduces the error peak value by 42.61% during sudden pedestrian turns. It can effectively predict the pedestrian's motion trajectory and has better adaptability under sudden pedestrian turns.