论文标题
深度学习的自动驾驶：对最新技术的调查
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
论文作者
论文摘要
自2004/05年度DARPA大挑战（农村）和2007年的城市挑战以来，自动驾驶一直是AI应用程序中最活跃的领域。几乎同时，深度学习取得了几位先驱，其中三个（也称为深度学习父亲），Hinton，Bengio和Lecun在2019年获得了ACM Turin奖。这是对具有深度学习方法的自动驾驶技术的调查。我们研究了自动驾驶系统的主要领域，例如感知，映射和本地化，预测，计划和控制，模拟，V2X和安全等，由于空间有限，我们将分析重点放在几个关键领域，即感知中的2D和3D对象检测，在相机中估算到相机估算，对数据的多个传感器融合，对数据的多个传感器融合，并且在数据范围内进行了驾驶和驾驶范围，并进行了行动模型和行为模型。
Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019. This is a survey of autonomous driving technologies with deep learning methods. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task level respectively, behavior modelling and prediction of vehicle driving and pedestrian trajectories.
