BEV特徴量への直接アテンションによるオンラインマッピングと行動予測の高速化
Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention
自動運転向けに、オンラインマップ推定の内部BEV特徴量を直接活用して軌道予測と統合する手法を提案し、推論速度を最大73%向上、予測精度を最大29%改善した。
著者: Xunjiang Gu, Guanyu Song, Igor Gilitschenski, Marco Pavone, Boris Ivanovic
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Understanding road geometry is a critical component of the autonomous vehicle (AV) stack. While high-definition (HD) maps can readily provide such information, they suffer from high labeling and maintenance costs. Accordingly, many recent works have proposed methods for estimating HD maps online from sensor data. The vast majority of recent approaches encode multi-camera observations into an intermediate representation, e.g., a bird's eye view (BEV) grid, and produce vector map elements via a decoder. While this architecture is performant, it decimates much of the information encoded in the intermediate representation, preventing downstream tasks (e.g., behavior prediction) from leveraging them. In this work, we propose exposing the rich internal features of online map estimation methods and show how they enable more tightly integrating online mapping with trajectory forecasting. In doing so, we find that directly accessing internal BEV features yields up to 73% faster inference speeds and up to 29% more accurate predictions on the real-world nuScenes dataset.
