Target-point Attention Transformer: A novel trajectory predict network for end-to-end autonomous driving

TL;DR

This paper introduces the Target-point Attention Transformer (TAT), a novel end-to-end autonomous driving model that leverages attention mechanisms for improved trajectory prediction and safety in complex urban scenarios.

Contribution

It presents a Transformer-based trajectory prediction network that effectively integrates perception features and target points without rule-based constraints.

Findings

Outperforms existing methods in accident reduction

Achieves state-of-the-art route completion in CARLA simulations

Demonstrates effectiveness in complex urban driving scenarios

Abstract

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.