2023/08/03 by Jingyu Du, Yang Zhao, Du, Jingyu +3
Computer Science · Engineering · #Advanced Neural Network Applications #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Robotics (cs.RO) #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2308.01496
openalex publication_date 2023/08/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the field of autonomous driving, there have been many excellent perception models for object detection, semantic segmentation, and other tasks, but how can we effectively use the perception models for vehicle planning? Traditional autonomous vehicle trajectory prediction methods not only need to obey traffic rules to avoid collisions, but also need to follow the prescribed route to reach the destination. In this paper, we propose a Transformer-based trajectory prediction network for end-to-end autonomous driving without rules called Target-point Attention Transformer network (TAT). We use the attention mechanism to realize the interaction between the predicted trajectory and the perception features as well as target-points. We demonstrate that our proposed method outperforms existing conditional imitation learning and GRU-based methods, significantly reducing the occurrence of accidents and improving route completion. We evaluate our approach in complex closed loop driving scenarios in cities using the CARLA simulator and achieve state-of-the-art performance.