カテゴリカル交通トランスフォーマー：トークン化潜在表現による解釈可能で多様な挙動予測
Categorical Traffic Transformer: Interpretable and Diverse Behavior Prediction with Tokenized Latent
交通予測モデルCTTを提案し、軌道予測とトークン化されたカテゴリカル予測を同時に出力することで、解釈可能な潜在空間と多様な挙動生成を実現し、予測精度でもSOTAを上回った。
著者: Yuxiao Chen, Sander Tonkens, Marco Pavone
分類: cs.LG, cs.CV, cs.RO
原文アブストラクト
Adept traffic models are critical to both planning and closed-loop simulation for autonomous vehicles (AV), and key design objectives include accuracy, diverse multimodal behaviors, interpretability, and downstream compatibility. Recently, with the advent of large language models (LLMs), an additional desirable feature for traffic models is LLM compatibility. We present Categorical Traffic Transformer (CTT), a traffic model that outputs both continuous trajectory predictions and tokenized categorical predictions (lane modes, homotopies, etc.). The most outstanding feature of CTT is its fully interpretable latent space, which enables direct supervision of the latent variable from the ground truth during training and avoids mode collapse completely. As a result, CTT can generate diverse behaviors conditioned on different latent modes with semantic meanings while beating SOTA on prediction accuracy. In addition, CTT's ability to input and output tokens enables integration with LLMs for common-sense reasoning and zero-shot generalization.
