Multi-Model Recurrent Neural Network Control for Lane Change Systems under Speed Variation

Abstract

A new multi-model recurrent neural network (RNN) control scheme is developed for autonomous vehicle lane-change maneuvering with longitudinal speed variation. Lateral motion control for lane-change maneuvering under longitudinal speed variation becomes challenging because the lateral vehicle dynamics is very involved. The literature has studied lane-change control using a bicycle dynamic model with fixed longitudinal speed. However, It rarely reported how a lane-change controller under variation of speed performs. In the paper, we develop an innovative scheme in which multiple RNNs are trained. And a probabilistic data association of their outputs is given as the command to the steering angle. Each RNN is trained by optimizing the corresponding model predictive control (MPC) with fixed vehicle speed. Further, the discrete probability distribution is used to avoid impractical RNN training for lane-change maneuvering with various vehicle speed variation scenarios. The proposed multi-model RNN control scheme is demonstrated through an application. The proposed system shows that it satisfies the constraints given in the design of MPCs and exhibit better control performance.