RiskMap: 都市走行環境における自動運転の運動計画のための統合運転コンテキスト表現
RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment
交通参加者に対する統計的認知を微分可能なリスク場として表現するRiskMapを提案し、サンプリングベースのプランナで訓練・評価して走行の安全性と滑らかさを改善した。
著者: Ren Xin, Sheng Wang, Yingbing Chen, Jie Cheng, Ming Liu, Jun Ma
分類: cs.RO
原文アブストラクト
Motion planning is a complicated task that requires the combination of perception, map information integration and prediction, particularly when driving in heavy traffic. Developing an extensible and efficient representation that visualizes sensor noise and provides basis to real-time planning tasks is desirable. We aim to develop an interpretable map representation, which offers prior of driving cost in planning tasks. In this way, we can simplify the planning process for dealing with complex driving scenarios and visualize sensor noise. Specifically, we propose a unified context representation empowered by deep neural networks. The unified representation is a differentiable risk field, which is an analytical representation of statistical cognition regarding traffic participants for downstream planning tasks. This representation method is nominated as RiskMap. A sampling-based planner is adopted to train and compare RiskMap generation methods. In this paper, the RiskMap generation tools and model structures are explored, the results illustrate that our method can improve driving safety and smoothness, and the limitation of our method is also discussed.
