Transformerネットワークを用いた自動運転のための軌道予測
Trajectory Prediction for Autonomous Driving Using a Transformer Network
周囲エージェントの軌道予測にTransformerベースのマルチモーダル枠組みを提案し、意味地図を畳み込みで取り込むとともに、道路外の非現実的な予測を罰する補助損失を導入してLyft l5kitで最高性能を達成した。
著者: Zhenning Li, Hao Yu
分類: cs.RO
原文アブストラクト
Predicting the trajectories of surrounding agents is still considered one of the most challenging tasks for autonomous driving. In this paper, we introduce a multi-modal trajectory prediction framework based on the transformer network. The semantic maps of each agent are used as inputs to convolutional networks to automatically derive relevant contextual information. A novel auxiliary loss that penalizes unfeasible off-road predictions is also proposed in this study. Experiments on the Lyft l5kit dataset show that the proposed model achieves state-of-the-art performance, substantially improving the accuracy and feasibility of the prediction outcomes.
