GET-DIPP: Graph-Embedded Transformer for Differentiable Integrated Prediction and Planning

TL;DR

GET-DIPP introduces a graph-embedded transformer that jointly predicts future trajectories of road agents and plans ego vehicle control commands, enhancing accuracy and social compliance for autonomous driving.

Contribution

The paper presents a novel planning-centric neural network with an agent interaction module and map topology processing, integrating prediction and planning in a differentiable framework.

Findings

Improved prediction accuracy over state-of-the-art methods

Enhanced planning quality with socially compliant trajectories

Effective integration of map topology via DGCNN

Abstract

Accurately predicting interactive road agents' future trajectories and planning a socially compliant and human-like trajectory accordingly are important for autonomous vehicles. In this paper, we propose a planning-centric prediction neural network, which takes surrounding agents' historical states and map context information as input, and outputs the joint multi-modal prediction trajectories for surrounding agents, as well as a sequence of control commands for the ego vehicle by imitation learning. An agent-agent interaction module along the time axis is proposed in our network architecture to better comprehend the relationship among all the other intelligent agents on the road. To incorporate the map's topological information, a Dynamic Graph Convolutional Neural Network (DGCNN) is employed to process the road network topology. Besides, the whole architecture can serve as a backbone for the Differentiable Integrated motion Prediction with Planning (DIPP) method by providing accurate prediction results and initial planning commands. Experiments are conducted on real-world datasets to demonstrate the improvements made by our proposed method in both planning and prediction accuracy compared to the previous state-of-the-art methods.