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- 2018
基于深度学习的自动驾驶技术综述
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Abstract:
该文在行人检测技术方面介绍了基于卷积神经网络（CNN）模型的目标识别、检测技术与改进的区域卷积神经网络（R-CNN）、任务辅助卷积神经网络（TA-CNN）模型技术。在立体匹配技术方面简述了基于孪生网络的立体匹配的深度学习模型技术。在多传感器融合技术方面回顾了基于深度学习网络的视觉传感器、雷达传感器与摄像机传感器的多源数据融合技术。在汽车控制技术方面分析了基于卷积神经网络实现无人驾驶车辆端到端的横向与纵向控制技术。深度学习技术在自动驾驶领域的感知层、决策层与控制层的广泛运用，不断地提高感知、检测、决策与控制的准确率，并取得一定的成功，分析表明深度学习技术将加速自动驾驶技术的发展速度，为自动驾驶成为现实带来巨大的可能性。
Abstract：This paper introduces target recognition and detection methods based on the convolutional neural network (CNN) model, the improved regions with convolutional neural network (R-CNN) and the task-assistant convolutional neural network (TA-CNN) model for pedestrian detection. This paper also describes stereo matching based on a deep learning model for stereo matching using the Siamese network. Multi-source data fusion is also introduced based on a vision sensor, a radar sensor and a camera using a deep learning network. The CNN is used for end-to-end horizontal and vertical control of autonomous vehicles. Deep learning is widely used in the perception level, decision-making level and control level in automatic driving systems to continuously improve the perception, detection, decision-making and control accuracy. Analyses show that deep learning will improve of autonomous driving systems.
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