MMFN: エンドツーエンド運転のためのマルチモーダル融合ネットワーク
MMFN: Multi-Modal-Fusion-Net for End-to-End Driving
カメラ・LiDAR・ベクター化HDマップを融合し、冗長情報を抑えつつ複数道路規則を考慮した専門家モジュールでエンドツーエンド運転性能を向上させた研究。
著者: Qingwen Zhang, Mingkai Tang, Ruoyu Geng, Feiyi Chen, Ren Xin, Lujia Wang
分類: cs.CV, cs.RO
原文アブストラクト
Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, camera and LiDAR inputs are fused through transformers for better driving performance. These inputs are normally further interpreted as high-level map information to assist navigation tasks. Nevertheless, extracting useful information from the complex map input is challenging, for redundant information may mislead the agent and negatively affect driving performance. We propose a novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in the end-to-end driving tasks. In addition, we design a new expert to further enhance the model performance by considering multi-road rules. Experimental results prove that both of the proposed improvements enable our agent to achieve superior performance compared with other methods.
