论文标题
部分可观测时空混沌系统的无模型预测
Research on Self-adaptive Online Vehicle Velocity Prediction Strategy Considering Traffic Information Fusion
论文作者
论文摘要
为了提高在线车辆速度预测（VVP）策略的预测准确性，为多种情况提供了一种与流量信息融合的自适应速度预测算法。最初，在共同模拟环境中建立了交通情况。此外，在交通场景中使用了一般回归神经网络（GRNN）的算法与自我车辆，前车和交通信号灯的数据集配对，这越来越提高了预测准确性。为了减轻算法的鲁棒性，通过粒子群优化（PSO）和K折叠跨验证来优化该策略，以实时找到神经网络的最佳参数，该参数构建了一种自适应在线PSO-GRNN VVP策略，并具有多种技术融合，以适应不同的操作上的各种操作。然后将自适应的在线PSO-GRNN VVP策略部署到各种模拟场景中，以测试其在各种操作情况下的功效。最后，模拟结果表明，与传统的GRNN VVP策略相比，在城市和高速公路场景中，预测准确性分别提高了27.8％和54.5％，其固定参数仅利用历史自我车辆速度数据集。
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.
