Robust Driving Intention Prediction Based on Multi-Stage Learning Under Vehicle–Infrastructure Cooperative Perception

In mixed traffic of human-driven vehicles (HDVs) and connected and automated vehicles (CAVs), it is essential to predict the driving intention of HDVs to avoid potential risks. Data quality is crucial to intention prediction under a cooperative vehicle-infrastructure system, whereas the data collection of HDVs relies on the vehicle-infrastructure cooperative perception, which is inevitably exposed to perception errors. In this paper, a robust driving intention prediction framework based on multi-stage learning is proposed in mixed traffic under the vehicle-infrastructure cooperative perception situation. To address this issue, different information sources from vehicles and traffic are considered to derive the implied vehicle dynamic interaction relation and traffic flow context. A feature extraction module is developed to respectively capture the local and global features based on convolutional neural network (CNN) for reducing the impact of noise, which ensures the prominent detailed and overall descriptions of driving intention can be comprehensively acquired. Then, the deep multi-scale technique and multi-layer perceptron network are introduced to further extract deep features, and improve the model adaptability by complementary feature learning mechanism and nonlinear mapping ability. Experiment results on a real-world dataset confirm the effectiveness of our proposal in reducing the impact of poor data quality and accurately predicting driving intention.