ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network

Songyan Zhao, Lingshan Chen () and Yongchao Huang
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Songyan Zhao: College of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 200335, China
Lingshan Chen: College of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 200335, China
Yongchao Huang: Shanghai Waylancer Automobile Technology Co., Ltd., Shanghai 201805, China

Data, 2024, vol. 9, issue 1, 1-16

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.

Keywords: ADAS functionality; simulation testing; BP neural networks; genetic algorithm (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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