{"title":"基于改进 BP 神经网络的 ADAS 仿真结果数据集处理","authors":"Songyan Zhao, Lingshan Chen, Yongchao Huang","doi":"10.3390/data9010011","DOIUrl":null,"url":null,"abstract":"The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. The data obtained from the simulation of target algorithms and vehicle models cannot be reasonably estimated. This problem affects subsequent vehicle improvement and parameter calibration. The authors relied on the simulation results of the AEB algorithm. We selected the BP Neural Network as the basis and improved it with a genetic algorithm optimized via a roulette algorithm. The regression evaluation indicators of the prediction results show that the GA-BP neural network has better prediction accuracy and generalization ability than the original BP neural network and other optimized BP neural networks. This GA-BP neural network also fills the Gap in Evaluation and Prediction Systems.","PeriodicalId":502371,"journal":{"name":"Data","volume":"48 7","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network\",\"authors\":\"Songyan Zhao, Lingshan Chen, Yongchao Huang\",\"doi\":\"10.3390/data9010011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. The data obtained from the simulation of target algorithms and vehicle models cannot be reasonably estimated. This problem affects subsequent vehicle improvement and parameter calibration. The authors relied on the simulation results of the AEB algorithm. We selected the BP Neural Network as the basis and improved it with a genetic algorithm optimized via a roulette algorithm. The regression evaluation indicators of the prediction results show that the GA-BP neural network has better prediction accuracy and generalization ability than the original BP neural network and other optimized BP neural networks. This GA-BP neural network also fills the Gap in Evaluation and Prediction Systems.\",\"PeriodicalId\":502371,\"journal\":{\"name\":\"Data\",\"volume\":\"48 7\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/data9010011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/data9010011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
自动驾驶模拟领域缺乏对模拟结果的评估和预测系统。从目标算法和车辆模型模拟中获得的数据无法得到合理估计。这一问题影响了后续的车辆改进和参数校准。作者依靠 AEB 算法的仿真结果。我们选择了 BP 神经网络作为基础,并通过轮盘算法优化遗传算法对其进行改进。预测结果的回归评价指标表明,GA-BP 神经网络比原始 BP 神经网络和其他优化后的 BP 神经网络具有更好的预测精度和泛化能力。该 GA-BP 神经网络也填补了评估和预测系统的空白。
ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network
The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. The data obtained from the simulation of target algorithms and vehicle models cannot be reasonably estimated. This problem affects subsequent vehicle improvement and parameter calibration. The authors relied on the simulation results of the AEB algorithm. We selected the BP Neural Network as the basis and improved it with a genetic algorithm optimized via a roulette algorithm. The regression evaluation indicators of the prediction results show that the GA-BP neural network has better prediction accuracy and generalization ability than the original BP neural network and other optimized BP neural networks. This GA-BP neural network also fills the Gap in Evaluation and Prediction Systems.