Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Sensor Fusion for Autonomous Vehicles
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensor Fusion for Autonomous Vehicles

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 9632

Special Issue Editor

Prof. Dr. Vijay John

E-Mail Website
Guest Editor
Toyota Technological Institute, Nagoya, Japan
Interests: deep learning; decision trees; swarm intelligence; probabilistic graphical models; environment perception; automated driving and vehicle localization; sensor fusion and calibration

Special Issue Information

Dear collegues,

Autonomous driving and advanced driver assistance systems (ADASs) research has witnessed rapid progress due to the need to improve the safety of road users. Typically, different sensors such as visible cameras, thermal cameras, LIDARs, milliwave radars, etc. are used in this research. In recent years, researchers have sought to increase the robustness of various autonomous driving applications through effective sensor fusion. New methods have been proposed to effectively fuse these different sensors for various applications such as object detection, semantic segmentation, behavior prediction etc. 

In this Special Issue, we invite contributions dealing with many aspects of sensor fusion for autonomous vehicles including multi-modal or multiple sensor registration or calibration, traditional and machine learning methods for sensor fusion-based perception, localization, navigation and control, and sensor fusion applications for autonomous driving.

Prof. Dr. Vijay John
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Multi-modal or multiple sensor registration and calibration
  • Traditional and machine learning methods for sensor fusion
  • Sensor fusion based perception, localization, navigation and control
  • Sensor fusion applications for autonomous driving
  • Visible camera, thermal camera, LIDAR, radar, stereo camera, GPS
  • Multi-camera setup applications

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 3150 KiB  
Article
Pedestrian Detection Using Multispectral Images and a Deep Neural Network
by Jason Nataprawira, Yanlei Gu, Igor Goncharenko and Shunsuke Kamijo
Sensors 2021, 21(7), 2536; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072536 - 04 Apr 2021
Cited by 28 | Viewed by 5708
Abstract
Pedestrian fatalities and injuries most likely occur in vehicle-pedestrian crashes. Meanwhile, engineers have tried to reduce the problems by developing a pedestrian detection function in Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. However, the system is still not perfect. A remaining problem in [...] Read more.
Pedestrian fatalities and injuries most likely occur in vehicle-pedestrian crashes. Meanwhile, engineers have tried to reduce the problems by developing a pedestrian detection function in Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. However, the system is still not perfect. A remaining problem in pedestrian detection is the performance reduction at nighttime, although pedestrian detection should work well regardless of lighting conditions. This study presents an evaluation of pedestrian detection performance in different lighting conditions, then proposes to adopt multispectral image and deep neural network to improve the detection accuracy. In the evaluation, different image sources including RGB, thermal, and multispectral format are compared for the performance of the pedestrian detection. In addition, the optimizations of the architecture of the deep neural network are performed to achieve high accuracy and short processing time in the pedestrian detection task. The result implies that using multispectral images is the best solution for pedestrian detection at different lighting conditions. The proposed deep neural network accomplishes a 6.9% improvement in pedestrian detection accuracy compared to the baseline method. Moreover, the optimization for processing time indicates that it is possible to reduce 22.76% processing time by only sacrificing 2% detection accuracy. Full article
(This article belongs to the Special Issue Sensor Fusion for Autonomous Vehicles)
►▼ Show Figures

Figure 1

16 pages, 6173 KiB  
Article
A Pedestrian Detection Algorithm Based on Score Fusion for Multi-LiDAR Systems
by Tao Wu, Jun Hu, Lei Ye and Kai Ding
Sensors 2021, 21(4), 1159; https://0-doi-org.brum.beds.ac.uk/10.3390/s21041159 - 07 Feb 2021
Cited by 13 | Viewed by 3195
Abstract
Pedestrian detection plays an essential role in the navigation system of autonomous vehicles. Multisensor fusion-based approaches are usually used to improve detection performance. In this study, we aimed to develop a score fusion-based pedestrian detection algorithm by integrating the data of two light [...] Read more.
Pedestrian detection plays an essential role in the navigation system of autonomous vehicles. Multisensor fusion-based approaches are usually used to improve detection performance. In this study, we aimed to develop a score fusion-based pedestrian detection algorithm by integrating the data of two light detection and ranging systems (LiDARs). We first evaluated a two-stage object-detection pipeline for each LiDAR, including object proposal and fine classification. The scores from these two different classifiers were then fused to generate the result using the Bayesian rule. To improve proposal performance, we applied two features: the central points density feature, which acts as a filter to speed up the process and reduce false alarms; and the location feature, including the density distribution and height difference distribution of the point cloud, which describes an object’s profile and location in a sliding window. Extensive experiments tested in KITTI and the self-built dataset show that our method could produce highly accurate pedestrian detection results in real-time. The proposed method not only considers the accuracy and efficiency but also the flexibility for different modalities. Full article
(This article belongs to the Special Issue Sensor Fusion for Autonomous Vehicles)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop