Implementation of Target Detection System for CARLA Driving Simulator Based on YOLOv7 Network
With the rapid development of autonomous driving technology,accurate identification of dynamic and static objects in simulated environment has become one of the key challenges to achieve highly automated driving.Simulation data sets have the characteristics of low acquisition cost,easy acquisition of extreme scenes,and strong continuity.Therefore,this paper adopts CARLA driving simulator as the experimental platform,combines the latest YOLOv7 target detection network,and improves the accuracy and speed of target detection by improving the network structure and optimizing the training strategy.This paper first introduces the basic architecture of CARLA simulator and the core principle of YOLOv7 algorithm,and then describes the design and implementation process of the experiment in detail,including data set preparation,network training and test evaluation.The experimental results show that the target detection method based on YOLOv7 has excellent performance in the automatic driving simulation environment,and can accurately identify various targets such as vehicles and pedestrians according to the input images.Finally,the paper discusses the significance of the experimental results,points out the potential application of the study in improving the sense of reality and safety of the autonomous driving simulator,and suggests the direction of future research.
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