Abstract
This study looks at the facilitation of computer vision and machine learning, which helps Autonomous vehicles (AVs) make pertinent decisions before entering a roundabout. A deep learning-based decision-making system (DBDM) is proposed in order to make a correct “Enter” or “Wait” decision when an AV enters a roundabout. In this regard, videos of human drivers negotiating different normal roundabouts, differing in terms of size, are employed alongside a range of different learning algorithms (e.g. VGG-16, Resnet-50 and Xception). In total, 130 videos captured at normal roundabouts were used to test the models, and VGG-16 performed best with an accuracy rate of 92.57% comparing with pervious work (GBIPA-SC-NR), thus suggesting that the proposed DBDM method can be applied effectively for AVs.
Supported by Loughborough University.
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Wang, W., Jiang, L., Lin, S., Fang, H., Meng, Q. (2020). Deep Learning-Based Decision Making for Autonomous Vehicle at Roundabout. In: Mohammad, A., Dong, X., Russo, M. (eds) Towards Autonomous Robotic Systems. TAROS 2020. Lecture Notes in Computer Science(), vol 12228. Springer, Cham. https://doi.org/10.1007/978-3-030-63486-5_16
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DOI: https://doi.org/10.1007/978-3-030-63486-5_16
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