Uncertainty-Aware Lane Change Prediction for Autonomous Driving Using LSTM Networks

This paper addresses the challenge of accurately predicting lane change maneuvers in autonomous driving while quantifying prediction uncertainty. We present an LSTM-based model that predicts three classes: lane keeping, left lane change, and right lane change. By considering the position, velocity, and acceleration of multiple vehicles, we enhance the accuracy of our predictions, achieving an overall accuracy of 97.10% with our base LSTM model. Our analysis of different uncertainty sources, including Gaussian noise injection, Dropout noise, and deep ensembles, reveals varying levels of uncertainty, with mean standard deviations ranging from 0.0084 to 0.1094. This highlights the model’s sensitivity to different types of perturbations and the importance of uncertainty quantification for robust lane change prediction. This contribution enables more informed decisionmaking in ADAS and paves the way for safer autonomous driving.