Research on Self-adaptive Online Vehicle Velocity Prediction Strategy Considering Traffic Information Fusion
This work addresses the need for more accurate and robust velocity prediction in autonomous driving systems, though it appears incremental as it builds on existing neural network methods with optimization techniques.
The paper tackled the problem of improving online vehicle velocity prediction accuracy by developing a self-adaptive strategy that fuses traffic information, resulting in prediction accuracy increases of 27.8% in urban scenarios and 54.5% in highway scenarios compared to a traditional method.
In order to increase the prediction accuracy of the online vehicle velocity prediction (VVP) strategy, a self-adaptive velocity prediction algorithm fused with traffic information was presented for the multiple scenarios. Initially, traffic scenarios were established inside the co-simulation environment. In addition, the algorithm of a general regressive neural network (GRNN) paired with datasets of the ego-vehicle, the front vehicle, and traffic lights was used in traffic scenarios, which increasingly improved the prediction accuracy. To ameliorate the robustness of the algorithm, then the strategy was optimized by particle swarm optimization (PSO) and k-fold cross-validation to find the optimal parameters of the neural network in real-time, which constructed a self-adaptive online PSO-GRNN VVP strategy with multi-information fusion to adapt with different operating situations. The self-adaptive online PSO-GRNN VVP strategy was then deployed to a variety of simulated scenarios to test its efficacy under various operating situations. Finally, the simulation results reveal that in urban and highway scenarios, the prediction accuracy is separately increased by 27.8% and 54.5% when compared to the traditional GRNN VVP strategy with fixed parameters utilizing only the historical ego-vehicle velocity dataset.