GET-DIPP: 予測と計画の微分可能統合のためのグラフ埋め込みトランスフォーマ
GET-DIPP: Graph-Embedded Transformer for Differentiable Integrated Prediction and Planning
周囲エージェントの履歴と地図情報から、エージェント間・時間軸の相互作用と道路網トポロジーをグラフとトランスフォーマで処理し、周囲のマルチモーダル予測と自車の制御指令を同時に出力する計画指向の予測ネットワークを提案。
著者: Jiawei Sun, Chengran Yuan, Shuo Sun, Zhiyang Liu, Terence Goh, Anthony Wong, Keng Peng Tee, Marcelo H. Ang
分類: cs.RO
原文アブストラクト
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.
