Probabilistic time-to-lane-change prediction on highways

Situation understanding and assessment is one of the key features for automated driving. To enable safe and comfortable motion planning, sensing the current situation is not sufficient but maneuver predictions as accurate as possible are required. The paper presents a novel approach of predicting the remaining time to an upcoming lane change of adjacent vehicles on a highway. The prediction is performed in a probabilistic way to cope with the variety in execution and duration of lane change maneuvers. Two quantile regression techniques, namely Linear Quantile Regression and Quantile Regression Forests, are applied and compared in terms of prediction error and accuracy on data gathered with different drivers on a fixed base driving simulator. The superior technique is also evaluated on a dataset recorded with a test vehicle to demonstrate its general applicability in real world scenarios.