自動運転のためのオープンセット交差点意図予測
Open-set Intersection Intention Prediction for Autonomous Driving
交差点での他車の意図予測をオープンセット問題として定式化し、時空間グラフと2つの相互補助注意モジュールで未知の交差点構造にも対応する手法を提案、実世界98交差点で有効性を示した。
著者: Fei Li, Xiangxu Li, Jun Luo, Shiwei Fan, Hongbo Zhang
分類: cs.RO, cs.CV
原文アブストラクト
Intention prediction is a crucial task for Autonomous Driving (AD). Due to the variety of size and layout of intersections, it is challenging to predict intention of human driver at different intersections, especially unseen and irregular intersections. In this paper, we formulate the prediction of intention at intersections as an open-set prediction problem that requires context specific matching of the target vehicle state and the diverse intersection configurations that are in principle unbounded. We capture map-centric features that correspond to intersection structures under a spatial-temporal graph representation, and use two MAAMs (mutually auxiliary attention module) that cover respectively lane-level and exitlevel intentions to predict a target that best matches intersection elements in map-centric feature space. Under our model, attention scores estimate the probability distribution of the openset intentions that are contextually defined by the structure of the current intersection. The proposed model is trained and evaluated on simulated dataset. Furthermore, the model, trained on simulated dataset and without any fine tuning, is directly validated on in-house real-world dataset collected at 98 realworld intersections and exhibits satisfactory performance,demonstrating the practical viability of our approach.
