GLAD: 複雑なサービス要求に対応する接地型階層型自動運転
GLAD: Grounded Layered Autonomous Driving for Complex Service Tasks
複数の目的地を巡る複雑なサービス要求に対し、サービス・行動・運動の3層を密結合した計画フレームワークGLADを提案し、知覚学習で接地することで安全かつ効率的な自動運転を実現した。
著者: Yan Ding, Cheng Cui, Xiaohan Zhang, Shiqi Zhang
分類: cs.RO
原文アブストラクト
Given the current point-to-point navigation capabilities of autonomous vehicles, researchers are looking into complex service requests that require the vehicles to visit multiple points of interest. In this paper, we develop a layered planning framework, called GLAD, for complex service requests in autonomous urban driving. There are three layers for service-level, behavior-level, and motion-level planning. The layered framework is unique in its tight coupling, where the different layers communicate user preferences, safety estimates, and motion costs for system optimization. GLAD is visually grounded by perceptual learning from a dataset of 13.8k instances collected from driving behaviors. GLAD enables autonomous vehicles to efficiently and safely fulfill complex service requests. Experimental results from abstract and full simulation show that our system outperforms a few competitive baselines from the literature.
