Research on MFP algorithm-based trajectory prediction model for intelligent connected vehicles
To address the low prediction accuracy in long-term trajectory prediction due to changing environments and interactive influences among vehicles,this paper proposes a multi-agent trajectory prediction model based on the Multiple Futures Predictor(MFP).First,the Symmetric Exponential Moving Average method is employed to remove outliers and smooth the trajectories.Then,the model utilizes Graph Convolutional Neural Network(GCN)for extracting interactive features between historical trajectories and future agents,encoding the interaction features.Finally,during the decoding process,the vehicle's own kinematic model is incorporated to generate dynamically feasible predicted trajectories.Experimental analysis is conducted on the publicly available NGSIM dataset.Our results demonstrate the model achieves trajectory prediction errors within 0.5 m.Compared to the results of other methods,the proposed model reduces ADE(Average Displacement Error)by 30.7％and FDE(Final Displacement Error)by 32.5％when predicting trajectories within 5 seconds,validating the effectiveness of the model and algorithm.
autonomous drivingvehicle trajectory predictiongraph neural networksfeature extractionMFP model
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