Multi-Feature-Based Integrated Vehicle Trajectory Prediction with Driving Intention Recognition

Abstract

Vehicle trajectory prediction and driving intention recognition are essential technologies for improving safety and optimizing traffic efficiency in autonomous driving systems. Although traditional LSTM models are effective in trajectory prediction, their complexity and computational requirements impede practical implementation efficiency. To overcome this challenge, this study proposes a hybrid model, MTF-GRU, which integrates multi-feature fusion. Initially, the datasets are preprocessed in this study through denoising, feature extraction, and timing extraction to capture vehicle information from single and fused multi-features. Subsequently, a GRU encoding-decoding model is developed. The encoder processes the feature data to generate context vectors, while the decoder employs a combination of recursive and teaching-driven input modes. Furthermore, a teaching rate control mechanism is integrated to dynamically convert context vectors into future trajectories. The proposed model is validated using the NGSIM datasets, demonstrating superior prediction performance with multi-feature inputs outperforming single features by reducing the average endpoint displacement error by 20.5%. Our model also achieves improved accuracy rates, particularly excelling in long-term predictions with an endpoint displacement error of only 2.31 meters at 5 seconds. Moreover, the overall accuracy rate for lane change intention recognition reaches 91.3%. The model's computational efficiency supports practical deployment in real-time autonomous systems, while future efforts will integrate multi-modal sensor data to enhance adaptability in complex urban scenarios and extreme conditions. Further validation will extend to diverse traffic environments and edge computing platforms to optimize real-world robustness.