A Survey on Lane Change Intention Prediction of Human Drivers

Lane changes are common maneuvers in daily and natural driving on public roads. Automated vehicles require information about the predicted motion of surrounding vehicles to be able plan their motion. This survey gives an overview on the state of the art on lane change prediction with respect to the datasets, methods for classification and features as inputs that are most frequently used. The most common outputs in terms of the
classification problem they solved (such as lane keeping and left and/or right lane change), definition of occurrence of a lane change and metrics used to evaluate the estimation results. Overall, 75 articles were included in the analysis, with 58 individual and 13 cumulative features being identified and assigned percentages with which they were used in the publications. Five classes of outputs were analysed in terms of accuracy of estimation results and its relation to the prediction time. In general, the prediction time was below 5 seconds and with increasing prediction time, the estimation accuracy decreased.