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In order to increase the prediction accuracy of the online vehicle velocityprediction (VVP) strategy, a self-adaptive velocity prediction algorithm fusedwith traffic information was presented for the multiple scenarios. Initially,traffic scenarios were established inside the co-simulation environment. Inaddition, the algorithm of a general regressive neural network (GRNN) pairedwith datasets of the ego-vehicle, the front vehicle, and traffic lights wasused in traffic scenarios, which increasingly improved the prediction accuracy.To ameliorate the robustness of the algorithm, then the strategy was optimizedby particle swarm optimization (PSO) and k-fold cross-validation to find theoptimal parameters of the neural network in real-time, which constructed aself-adaptive online PSO-GRNN VVP strategy with multi-information fusion toadapt with different operating situations. The self-adaptive online PSO-GRNNVVP strategy was then deployed to a variety of simulated scenarios to test itsefficacy under various operating situations. Finally, the simulation resultsreveal that in urban and highway scenarios, the prediction accuracy isseparately increased by 27.8% and 54.5% when compared to the traditional GRNNVVP strategy with fixed parameters utilizing only the historical ego-vehiclevelocity dataset.

Research on Self-adaptive Online Vehicle Velocity Prediction Strategy Considering Traffic Information Fusion