Cooperation-Aware Lane Change Control in Dense Traffic

Cooperation-A ware Lane Change Control in Dense Traffic Sangjae Bae 1, Dhruv Saxena 2, Alireza Nakhaei 3, Chiho Choi 3, Kikuo Fujimura 3, and Scott Moura 1 Abstract -- This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of human drivers. This paper especially focuses on heavy traffic where vehicles cannot change lane without cooperating with other drivers. In this case, classical robust controls may not apply since there is no "safe" area to merge to. That said, modeling complex and interactive human behaviors is nontrivial 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 human drivers in response to potential actions of the autonomous vehicle, which are then be 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, showing a strong potential of the proposed framework. I NTRODUCTION An autonomous-driving vehicle is no longer a futuristic concept and extensive researches have been conducted in various aspects, spanning from localization, perceptions, and controls to implementations and validations. Particularly from the perspective of control engineers, designing a controller that secures safety, in various traffic conditions, such as driving on arterial-road/highway in free-flow/dense traffic with/without traffic lights, has been a principal research focus.