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A Survey on Lane Change Intention Prediction of Human Drivers
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DOI：10.1109/TIV.2026.3664957.png)
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Abstract
En 中文
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, 76 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. The analysis of the latest publications revealed an increasing interest in an already established prediction method, recurrent neural networks, but revealed also that newer methodologies like transformer networks have been relatively often used in the last five years showing that there is still room for experimenting with this task. In general, the prediction time was below 5 seconds and with increasing prediction time, the estimation accuracy decreased.
Keywords:
Motion prediction
intention prediction
lane change prediction
motion planning
decision making
automated driving
autonomous driving
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IF:
14.3
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1.2K
Citations:
1.2W
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