The Library
Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors
Tools
Tools
Tools
Arnold, Eduardo, Dianati, Mehrdad, de Temple, Robert and Fallah, Saber (2020) Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors. IEEE Transactions on Intelligent Transportation Systems . doi:10.1109/TITS.2020.3028424 (In Press)
|
PDF
WRAP-Cooperative-perception-3D-object-detection-Dianati-2020.pdf - Accepted Version - Requires a PDF viewer. Download (2427Kb) | Preview |
Official URL: https://doi.org/10.1109/TITS.2020.3028424
Abstract
3D object detection is a common function within the perception system of an autonomous vehicle and outputs a list of 3D bounding boxes around objects of interest. Various 3D object detection methods have relied on fusion of different sensor modalities to overcome limitations of individual sensors. However, occlusion, limited field-of-view and low-point density of the sensor data cannot be reliably and cost-effectively addressed by multi-modal sensing from a single point of view. Alternatively, cooperative perception incorporates information from spatially diverse sensors distributed around the environment as a way to mitigate these limitations. This article proposes two schemes for cooperative 3D object detection using single modality sensors. The early fusion scheme combines point clouds from multiple spatially diverse sensing points of view before detection. In contrast, the late fusion scheme fuses the independently detected bounding boxes from multiple spatially diverse sensors. We evaluate the performance of both schemes, and their hybrid combination, using a synthetic cooperative dataset created in two complex driving scenarios, a T-junction and a roundabout. The evaluation shows that the early fusion approach outperforms late fusion by a significant margin at the cost of higher communication bandwidth. The results demonstrate that cooperative perception can recall more than 95% of the objects as opposed to 30% for single-point sensing in the most challenging scenario. To provide practical insights into the deployment of such system, we report how the number of sensors and their configuration impact the detection performance of the system.
| Item Type: | Journal Article | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > Q Science (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TL Motor vehicles. Aeronautics. Astronautics |
|||||||||
| Divisions: | Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
| Library of Congress Subject Headings (LCSH): | Automated vehicles, Computer vision, Image processing, Template matching (Digital image processing), Pattern recognition systems, Machine learning | |||||||||
| Journal or Publication Title: | IEEE Transactions on Intelligent Transportation Systems | |||||||||
| Publisher: | IEEE | |||||||||
| ISSN: | 1524-9050 | |||||||||
| Official Date: | 16 October 2020 | |||||||||
| Dates: |
|
|||||||||
| DOI: | 10.1109/TITS.2020.3028424 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | In Press | |||||||||
| Publisher Statement: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
