SSN: Sequential Spatial Network for Trajectory Prediction in Bird's-Eye View

Autonomous driving has been an active area of research and development, with various strategies being explored for decision-making in autonomous vehicles. Rule-based systems, decision trees, Markov decision processes, and Bayesian networks have been some of the popular methods used to tackle the complexities of traffic conditions and avoid collisions. However, with the emergence of deep learning, many researchers have turned towards Bird's-Eye View based methods to improve the precision of trajectory prediction. Despite the promising results achieved by some CNN-based methods, the failure to establish correlations between sequential images often leads to more collisions. In this paper, we propose an attention-based method that overcomes the limitation by establishing feature correlations between regions in Bird's-Eye View images using variants of multi-head attention. Our method combines the advantages of CNN with different kernel sizes in capturing regional features with multi-head self-attention structure to enhance the relationship between different local areas. Our method takes 'Bird's Eye View' graphs generated from camera and LiDAR sensors as input, and simulates the position (x, y) and head offset angle (Yaw) to generate future trajectories. Each trajectory consists of 12 way-points and each point contains the above position and yaw information. Experiment results demonstrate that our proposed method outperforms existing vision-based strategies, achieving an average of only 12.4 collisions per 1000 miles of driving distance on the L5kit test set. This significantly improves the success rate of collision avoidance and provides a potential solution for autonomous driving. We have uploaded the GitHub link¹¹Github link: https://github.com/HaynesLi/SSN of this algorithm.