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A framework to analyze noise factors of automotive perception sensors
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Chan, Pak Hung, Dhadyalla, Gunwant and Donzella, Valentina (2020) A framework to analyze noise factors of automotive perception sensors. IEEE Sensors Letters, 4 (6). pp. 1-4. doi:10.1109/LSENS.2020.2996428 ISSN 1530-437X.
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WRAP-A-framework-analyze-noise-factors-automotive-perception-sensors-Donzella-2020.pdf - Accepted Version - Requires a PDF viewer. Download (984Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/LSENS.2020.2996428
Abstract
Automated vehicles (AVs) are one of the breakthroughs of this century. The main argument to support their development is increased safety and reduction of human and economic losses; however, to demonstrate that AVs are safer than human drivers billions of miles of testing are required. Thus, realistic simulation and virtual testing of AV systems and sensors are crucial to accelerate the technological readiness. In particular, perception sensor measurements are affected by uncertainties due to noise factors; these uncertainties need to be included in simulations. This letter presents a framework to exhaustively analyze and simulate the effect of the combination of noise factors on sensor data. We applied the framework to analyze one sensor, the light detection and ranging (LiDAR), but it can be easily adapted to study other sensors. Results demonstrate that single noise factor analysis gives an incomplete knowledge of measurement degradation and perception is dramatically hindered when more noises are combined. The proposed framework is a powerful tool to predict the degradation of AV sensor performance.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TS Manufactures | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| Library of Congress Subject Headings (LCSH): | Automated guided vehicle systems, Optical radar, Radar air traffic control systems, Noise control, Remote sensing | ||||||
| Journal or Publication Title: | IEEE Sensors Letters | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 1530-437X | ||||||
| Official Date: | June 2020 | ||||||
| Dates: |
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| Volume: | 4 | ||||||
| Number: | 6 | ||||||
| Page Range: | pp. 1-4 | ||||||
| DOI: | 10.1109/LSENS.2020.2996428 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | © 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 | ||||||
| Date of first compliant deposit: | 19 June 2020 | ||||||
| Date of first compliant Open Access: | 19 June 2020 |
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