Abstract
The challenge in ensuring the reliability of autonomous vehicles is full awareness of the surrounding environment and high-precision steering control. The latest solutions to this challenge include deep learning technologies that provide end-to-end solutions to predict steering angles directly from environmental cognition information with high accuracy. Under the background of 5G technology, edge device has certain computing power, which can reduce the load of on-board computing equipment. In this paper, we present a new distributed perception-decision network model. This model allows the network’s computing tasks to be offloaded to the edge computing devices to reduce the consumption of vehicle-mounted computing devices. The feasibility of the model is verified by experiments. Compared with the existing methods, the model also has a higher accuracy of steering prediction.
This work was supported in part by the Natural Science Foundation of China under Grant 61876023 and Grant 61902035, and in part by the Natural Science Foundation of Beijing under Grant 4181002.
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Liu, T., Li, J., Yuan, Q. (2020). Predicting Steering for Autonomous Vehicles Based on Crowd Sensing and Deep Learning. In: Hsu, CH., Kallel, S., Lan, KC., Zheng, Z. (eds) Internet of Vehicles. Technologies and Services Toward Smart Cities. IOV 2019. Lecture Notes in Computer Science(), vol 11894. Springer, Cham. https://doi.org/10.1007/978-3-030-38651-1_12
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