Predictive Lane Change Decision Making Using Bidirectional Long Shot-Term Memory for Autonomous Driving on Highways

This paper presents a lane change decision algorithm for predictive decision-making for an autonomous vehicle using a Recurrent Neural Network (RNN) with a Bidirectional Long Short-Term Memory (Bi-LSTM) cell. The proposed decision-making algorithm was trained and validated by driving data collected by vision, laser scanners, and chassis sensors of autonomous vehicles. The input features for the Bi-LSTM based RNN consist of the clearance and relative velocity with the surrounding target vehicles, lane measurements, and the velocity of the autonomous vehicle. The output features are configured to generate the probability of three maneuvers, left lane change, right lane change, and lane-keeping. The Bi-LSTM based RNN is configured to decide in advance two seconds before lane changes by using two seconds of observation. The collected 20,108 datasets were accumulated in global coordinates. After processing and resampling the collected datasets, 1,120, 320, and 160 datasets were generated to train, validate, and test the Bi-LSTM based RNN. The proposed algorithm was evaluated by a case study and a driving data-based prediction accuracy analysis. The results of the predictive lane change decision by the proposed algorithm have been shown to be more accurate and similar to a driver than previous approaches.