自己教師あり学習で多段階と一括の軌道予測のギャップを埋める
Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision
将来の軌道を複数セグメントに分けて連鎖させ、中間セグメントからの追加予測や潜在文脈の想像・過去予測を自己教師ありで行う予測器を提案し、INTERACTIONデータセットで競争力のある精度を達成した。
著者: Faris Janjoš, Max Keller, Maxim Dolgov, J. Marius Zöllner
分類: cs.RO, cs.AI
原文アブストラクト
Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in a one-shot or step-wise manner. Although one-shot approaches are usually preferred for their simplicity, they relinquish powerful self-supervision schemes that can be constructed by chaining multiple time-steps. We address this issue by proposing a middle-ground where multiple trajectory segments are chained together. Our proposed Multi-Branch Self-Supervised Predictor receives additional training on new predictions starting at intermediate future segments. In addition, the model 'imagines' the latent context and 'predicts the past' while combining multi-modal trajectories in a tree-like manner. We deliberately keep aspects such as interaction and environment modeling simplistic and nevertheless achieve competitive results on the INTERACTION dataset. Furthermore, we investigate the sparsely explored uncertainty estimation of deterministic predictors. We find positive correlations between the prediction error and two proposed metrics, which might pave way for determining prediction confidence.
