Advanced Research in Automatic Driving for Electric Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 1769

Special Issue Editors

State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: routing protocols of IoVs; on-demand services in 6G; simulation platform and test system for 6G; heterogeneous data fusion
Special Issues, Collections and Topics in MDPI journals
Department of Traffic Engineering, College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: traffic operation; transportation data mining; statistical analysis; traffic safety
Special Issues, Collections and Topics in MDPI journals
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: vehicular networks; 6G; intelligent transportation systems; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Despite the rapid development of autonomous driving technology, there is still a problem of frequent accidents in autonomous driving. Moreover, the large-scale adoption of autonomous vehicles is still out of reach. What is the bottleneck in this? What is the effective solution to ensure the safety and commercial landing of autonomous driving? Is it an effective solution to continuously upgrade artificial intelligence technology and perception equipment to make autonomous vehicles more intelligent? Is it an effective solution to develop intelligent vehicle infrastructure cooperative systems (IVICS) and updated autonomous driving by integrating communication, perception, and computing? This Special Issue aims to is to discuss safe and reliable autonomous driving technology and promote the development of autonomous driving technology.

  • Intelligent vehicle infrastructure cooperative technology;
  • Internet of vehicle technology for autonomous driving;
  • The application of machine learning in autonomous driving;
  • The application of blockchain in autonomous driving;
  • Safety technology for autonomous driving safety;
  • Multi-source data fusion;
  • Traffic situation awareness technology.

Dr. Lina Zhu
Dr. Yajie Zou
Dr. Wenwei Yue
Guest Editors

Manuscript Submission Information

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Keywords

  • autonomous driving
  • intelligent vehicle infrastructure cooperative technology
  • internet of vehicle
  • artificial intelligence

Published Papers (1 paper)

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Research

16 pages, 16999 KiB  
Article
Segmentation Can Aid Detection: Segmentation-Guided Single Stage Detection for 3D Point Cloud
by Xueqing Wang, Diankun Zhang, Haoyu Niu and Xiaojun Liu
Electronics 2023, 12(8), 1783; https://doi.org/10.3390/electronics12081783 - 10 Apr 2023
Viewed by 1214
Abstract
Detecting accurate 3D bounding boxes from point cloud data plays an essential role in autonomous driving. However, improving performance requires more complex models, which come with high memory and computational cost. In this work, we design a Segmentation-Guided Auxiliary Network (SGAN) to improve [...] Read more.
Detecting accurate 3D bounding boxes from point cloud data plays an essential role in autonomous driving. However, improving performance requires more complex models, which come with high memory and computational cost. In this work, we design a Segmentation-Guided Auxiliary Network (SGAN) to improve the localization quality of detection. The points from different levels are concatenated to generate the multi-scale feature for the points used for prediction, i.e., candidate points. SGAN is jointly optimized by two tasks of candidate points—segmentation and center estimation—and it is only used in training and therefore introduces no extra computation in the inference stage. Furthermore, we consider that point-based detectors suffer from the outline points of sampling, so we explore the correlation between the data and propose the Point Cloud External Attention (PCEA) to extract the semantic features with a low memory cost. Our method SGSSD achieves a large margin against the baseline on the KITTI and Waymo datasets, and it runs at 25 FPS for inference on the KITTI test set with a single NVIDIA RTX 3090. Full article
(This article belongs to the Special Issue Advanced Research in Automatic Driving for Electric Vehicles)
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