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- W4382052879 abstract "In the realm of big data and cloud computing, the autonomous vehicle is an extremely promising and complex research topic, which is driven primarily by computer systems that employ AI technology, radar, GPS, visual computing, cloud computing, and other technologies to operate. autonomous vehicles use LIDAR to sense road information, and highly accurate maps are integrated with V2X (vehicle networking technology) to recognize relevant information such as traffic lights and speed limit signs. However, in some remote areas where map information needs to be completed, recognition of traffic lights and traffic signs cannot rely on positioning to achieve this. This paper uses the Efficientdet-d1 target detection algorithm built on Pytorch to simulate autonomous vehicles sensing pedestrian, vehicle, traffic light, and traffic sign information. This target detection algorithm uses EfficientNet-B1 as the backbone network and enhances the feature extraction process using four stacked BiFPN modules. The method involves using the open source big dataset LISA traffic light dataset and GTSRB German traffic sign dataset to train the model. Considering the uneven distribution of samples in the dataset, these classes are distributed into three Efficientdet-d1 target detection frameworks for pedestrians and various types of vehicles, traffic lights, and traffic signs, respectively. A multi-threaded approach allows the three detection processes to be executed simultaneously. The detection results are stored in a queue before aggregation for mapping to improve the speed of single-image execution. The experiments show the three prediction networks used achieved better results overall. The method proposed in this paper is a practical guide for autonomous vehicles to make road condition judgments during driving in remote areas." @default.
- W4382052879 created "2023-06-27" @default.
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- W4382052879 date "2023-04-26" @default.
- W4382052879 modified "2023-09-23" @default.
- W4382052879 title "Efficientdet Based Visial Perception for Autonomous Driving" @default.
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- W4382052879 doi "https://doi.org/10.1109/icccbda56900.2023.10154715" @default.
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