ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (9): 1781-1799.doi: 10.7544/issn1000-1239.2020.20200255

Special Issue: 2020边缘计算专题

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Multi-Modality Fusion Perception and Computing in Autonomous Driving

Zhang Yanyong, Zhang Sha, Zhang Yu, Ji Jianmin, Duan Yifan, Huang Yitong, Peng Jie, Zhang Yuxiang   

  1. (School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027)
  • Online:2020-09-01
  • Supported by: 
    This work was supported by the National Major Program for Technological Innovation 2030—New Generation Artificial Intelligence (2018AAA0100500).

Abstract: The goal of autonomous driving is to provide a safe, comfortable and efficient driving environment for people. In order to have wide-spread deployment of autonomous driving systems, we need to process the sensory data from multiple streams in a timely and accurate fashion. The challenges that arise are thus two-fold: leveraging the multiple sensors that are available on autonomous vehicles to boost the perception accuracy; jointly optimizing perception models and the underlying computing models to meet the real-time requirements. To address these challenges, this paper surveys the latest research on sensing and edge computing for autonomous driving and presents our own autonomous driving system, Sonic. Specifically, we propose a multi-modality perception model, ImageFusion, that combines the lidar data and camera data for 3D object detection, and a computational optimization framework, MPInfer.

Key words: autonomous driving, perception, multi-modality, fusion, edge computing, computational optimization

CLC Number: