Reinforcement Learning for Dynamic Optimization of Lane Change Intention Recognition for Transportation Networks
Abstract
Advance driver assistance systems (ADAS) swiftly and effectively detect oncoming cars’ lanes-changing intentions in intelligent transportation, supporting decision support and safety. Current techniques fail to account for vehicle interactions and trajectory data temporal dependencies; hence this research proposes a multi-model fusion-based lane-changing intention recognition framework for intelligent transportation. Using actual vehicle trajectory data from a dataset, the suggested model is verified and contrasted with several well used baseline models. According to the experimental findings, the lane change intention detection technique can greatly increase prediction accuracy by fusing attention processes, reinforcement learning-based CRF, and vehicle interaction data. The system’s main components are input processing and lane-changing intention recognition. Vehicle trajectory data is cleaned, labelled, sliced, and one-hot encoded during input processing BiLSTM-F model detects driver lane-change intent, enhanced by incorporating attention mechanism to the Bidirectional Long Short-Term Memory (BiLSTM) network, the model may give changing weights to input processing section output. This lets the model focus on lane-changing intention-affecting factors. Finally, a Reinforcement Learning-based Conditional Random Field (CRF) efficiently determines the globally optimal lane-changing intention. This field fully represents input data temporal interdependence. The model was trained and tested on the public NGSIM dataset. Validation results show it can achieve up to 97.19% accuracy and predict a vehicle’s lane change intention with 94.16% accuracy, two seconds before the actual maneuver occurs. The suggested model outperforms baseline lane-changing intention recognition models in terms of accuracy, loss performance, F1 score, and stability.