{
  "id": "2006.06091",
  "version": "v1",
  "published": "2020-06-10T22:21:57.000Z",
  "updated": "2020-06-10T22:21:57.000Z",
  "title": "Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies",
  "authors": [
    "Yu Huang",
    "Yue Chen"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-06-10T22:21:57.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "autonomous driving",
      "state-of-art technologies",
      "darpa grand challenges",
      "3d object detection",
      "multiple sensor fusion"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}