文脈認識を高める運動予測モデルCAB
Raising context awareness in motion forecasting
軌道予測モデルが現在の動きに過度に依存し意味的文脈を活用できていない問題に対し、文脈利用を促す学習手順を備えたCABを提案し、予測の時間的一貫性を測る新指標も導入した。
著者: Hédi Ben-Younes, Éloi Zablocki, Mickaël Chen, Patrick Pérez, Matthieu Cord
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Learning-based trajectory prediction models have encountered great success, with the promise of leveraging contextual information in addition to motion history. Yet, we find that state-of-the-art forecasting methods tend to overly rely on the agent's current dynamics, failing to exploit the semantic contextual cues provided at its input. To alleviate this issue, we introduce CAB, a motion forecasting model equipped with a training procedure designed to promote the use of semantic contextual information. We also introduce two novel metrics - dispersion and convergence-to-range - to measure the temporal consistency of successive forecasts, which we found missing in standard metrics. Our method is evaluated on the widely adopted nuScenes Prediction benchmark as well as on a subset of the most difficult examples from this benchmark. The code is available at github.com/valeoai/CAB
