RNNとMPCを組み合わせた協調を考慮した密集交通での車線変更制御
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
RNNで他車の協調的な反応を予測し、MPCの安全制約に組み込むことで、密集交通でもリアルタイムに車線変更できる制御手法を提案した。
著者: Sangjae Bae, Dhruv Saxena, Alireza Nakhaei, Chiho Choi, Kikuo Fujimura, Scott Moura
分類: cs.RO, cs.AI
原文アブストラクト
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers. This paper focuses on heavy traffic where vehicles cannot change lanes without cooperating with other drivers. In this case, classical robust controls may not apply since there is no safe area to merge to without interacting with the other drivers. That said, modeling complex and interactive human behaviors is highly non-trivial from the perspective of control engineers. We propose a mathematical control framework based on Model Predictive Control (MPC) encompassing a state-of-the-art Recurrent Neural network (RNN) architecture. In particular, RNN predicts interactive motions of other drivers in response to potential actions of the autonomous vehicle, which are then systematically evaluated in safety constraints. We also propose a real-time heuristic algorithm to find locally optimal control inputs. Finally, quantitative and qualitative analysis on simulation studies are presented to illustrate the benefits of the proposed framework.
