自動運転のための固定次元・置換不変な状態表現
Fixed-Dimensional and Permutation Invariant State Representation of Autonomous Driving
周囲車両をニューラルネットで符号化して加算・連結することで、車両数が可変でも固定次元かつ置換不変な状態表現を実現し、近似誤差を62.2%削減した。
著者: Jingliang Duan, Dongjie Yu, Shengbo Eben Li, Wenxuan Wang, Yangang Ren, Ziyu Lin, Bo Cheng
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
In this paper, we propose a new state representation method, called encoding sum and concatenation (ESC), for the state representation of decision-making in autonomous driving. Unlike existing state representation methods, ESC is applicable to a variable number of surrounding vehicles and eliminates the need for manually pre-designed sorting rules, leading to higher representation ability and generality. The proposed ESC method introduces a representation neural network (NN) to encode each surrounding vehicle into an encoding vector, and then adds these vectors to obtain the representation vector of the set of surrounding vehicles. By concatenating the set representation with other variables, such as indicators of the ego vehicle and road, we realize the fixed-dimensional and permutation invariant state representation. This paper has further proved that the proposed ESC method can realize the injective representation if the output dimension of the representation NN is greater than the number of variables of all surrounding vehicles. This means that by taking the ESC representation as policy inputs, we can find the nearly optimal representation NN and policy NN by simultaneously optimizing them using gradient-based updating. Experiments demonstrate that compared with the fixed-permutation representation method, the proposed method improves the representation ability of the surrounding vehicles, and the corresponding approximation error is reduced by 62.2%.
