Traffic Light Detection and Recognition for Self Driving Cars using Deep Learning: Survey

Aswathy Madhu, S. Sruthy
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引用次数: 1

Abstract

Self-driving cars has the potential to revolutionize urban mobility by providing sustainable, safe, and convenient and congestion free transportability. Autonomous driving vehicles have become a trend in the vehicle industry. Many driver assistance systems (DAS) have been presented to support these automatic cars. This vehicle autonomy as an application of AI has several challenges like infallibly recognizing traffic lights, signs, unclear lane markings, pedestrians, etc. These problems can be overcome by using the technological development in the fields of Deep Learning, Computer Vision due to availability of Graphical Processing Units (GPU) and cloud platform. By using deep learning, a deep neural network based model is proposed for reliable detection and recognition of traffic lights (TL).
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基于深度学习的自动驾驶汽车红绿灯检测与识别研究
自动驾驶汽车通过提供可持续、安全、方便和无拥堵的交通方式,有可能彻底改变城市交通。自动驾驶汽车已经成为汽车行业的一种趋势。许多驾驶员辅助系统(DAS)已经出现,以支持这些自动驾驶汽车。作为人工智能的应用,这种自动驾驶汽车面临着一些挑战,比如准确识别交通灯、标志、不清晰的车道标记、行人等。由于图形处理单元(GPU)和云平台的可用性,这些问题可以通过利用深度学习、计算机视觉领域的技术发展来克服。利用深度学习技术,提出了一种基于深度神经网络的交通信号灯可靠检测与识别模型。
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