Self-Supervised Point Cloud Prediction for Autonomous Driving

Pose prediction and trajectory forecasting represent pivotal tasks in the realm of autonomous driving, crucially enhancing the planning and decision-making capabilities of self-driving vehicles. However, a prevailing challenge is that many existing algorithms for these tasks necessitate supervised training, demanding substantial human effort and computational resources. To alleviate this resource-intensive burden, this paper introduces an innovative method for predicting future object poses and trajectories in a 3D space, obviating the requirement for manual annotations. The enhanced self-supervised 3D point cloud prediction algorithm proposed in this study incorporates an 3D Action Attention module, augmenting TCNet’s proficiency in extracting vital spatiotemporal and motion information from continuous point cloud range images. Additionally, 3D Octave Convolution is harnessed to mitigate the computational overhead introduced by the 3D Action Attention module, consequently accelerating the model’s inference speed. This advanced self-supervised 3D point cloud prediction algorithm is denoted as TSMNet. TSMNet outperforms the baseline TCNet and several SOTA 3D point cloud prediction models when evaluated on the KITTI Odometry dataset. Moreover, it showcases robust generalization capabilities in unfamiliar environments. Notably, TSMNet can predict future point cloud data for five frames in a mere 33 milliseconds, surpassing the frame rate of typical LiDAR sensors, which typically operate at 10Hz. Furthermore, when integrated with a point cloud clustering and tracking algorithm, the improved self-supervised 3D point cloud prediction algorithm facilitates the extraction of object poses and trajectories. The performance metrics of the point cloud clustering and tracking algorithm attain remarkable levels of accuracy, with a Multiple Object Tracking Accuracy (MOTA) of 86.12% and a Multiple Object Tracking Precision (MOTP) of 91.01% on the KITTI dataset.