自動運転のための共有クロスモーダル軌道予測
Shared Cross-Modal Trajectory Prediction for Autonomous Driving
複数のセンサ入力を共有潜在空間に埋め込むクロスモーダル学習により、テスト時は単一モダリティでも高精度な軌道予測を実現する枠組みを提案した論文。
著者: Chiho Choi, Joon Hee Choi, Srikanth Malla, Jiachen Li
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Predicting future trajectories of traffic agents in highly interactive environments is an essential and challenging problem for the safe operation of autonomous driving systems. On the basis of the fact that self-driving vehicles are equipped with various types of sensors (e.g., LiDAR scanner, RGB camera, radar, etc.), we propose a Cross-Modal Embedding framework that aims to benefit from the use of multiple input modalities. At training time, our model learns to embed a set of complementary features in a shared latent space by jointly optimizing the objective functions across different types of input data. At test time, a single input modality (e.g., LiDAR data) is required to generate predictions from the input perspective (i.e., in the LiDAR space), while taking advantages from the model trained with multiple sensor modalities. An extensive evaluation is conducted to show the efficacy of the proposed framework using two benchmark driving datasets.
