A XGBoost-Based Lane Change Prediction on Time Series Data Using Feature Engineering for Autopilot Vehicles

A XGBoost-Based Lane Change Prediction on Time Series Data Using Feature Engineering for Autopilot Vehicles

IEEE Transactions on Intelligent Transportation Systems

10.1109/TITS.2022.3170628

http://dx.doi.org/10.1109/tits.2022.3170628

Yi Zhang, Xiupeng Shi, Sheng Zhang, Anuj Abraham

Mechanical Engineering, Computer Science Applications, Automotive Engineering

03 May 2022

Zhang, Y., Shi, X., Zhang, S., & Abraham, A. (2022). A XGBoost-Based Lane Change Prediction on Time Series Data Using Feature Engineering for Autopilot Vehicles. IEEE Transactions on Intelligent Transportation Systems, 1–14. https://doi.org/10.1109/tits.2022.3170628

Road accidents wreck lives. Could technology stop them from happening? Driving better road safety with technology and artificial intelligence are the key elements considered by several carmakers. The key aspect of transportation in the future is to build an ecosystem comprising autonomous, connected, electric and shared mobility. The evolution of autonomous vehicles (AVs) can potentially aid transportation to people and be deployed to resolve mobility-related pain for drivers and safety on roads while changing lanes. Thus, the intelligent assistance system should be smart enough to track such vehicles while deviating into another lane. In this paper, we propose a lane change prediction framework for feature learning, with the aim to have a deep and comprehensive understanding of lane change behaviors, meanwhile, reach a high performance based on the selected features. A time-step dataset with more than 1000 features is constructed from vehicle trajectory data. To identify the key features involved in the original feature set, an XGBoost-based three-step feature learning algorithm is proposed, which integrates the feature importance ranking, metric selection and recursive feature elimination. After analyzing the accuracy of test data from different time segment positions, the sliding window method is applied on a time-step dataset with filtered features to properly select time segments, which are flattened into corresponding time-series dataset for model prediction. In our case studies, a publicly available dataset, Next Generation SIMulation (NGSIM), is adopted to conduct experiments of feature learning and lane change prediction, where we achieved a new state-of-art accuracy with 97.6% under the time-series data of 75 selected features and 1-second window size with predictor XGBoost after adopting proposed three-step method, which is superior to the other state-of-the-art feature selection methods.

Publisher Copyright

There was no specific funding for the research done

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