Track, GoPro, and Prescan Testing of an ADAS Camera

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Event
WCX SAE World Congress Experience
Authors Abstract
Content
In order to validate the operation of advanced driver assistance systems (ADAS), tests must be performed that assess the performance of the system in response to different scenarios. Some of these systems are designed for crash-imminent situations, and safely testing them requires large stretches of controlled pavement, expensive surrogate targets, and a fully functional vehicle. As a possible more-manageable alternative to testing the full vehicle in these situations, this study sought to explore whether these systems could be isolated, and tests could be performed on a bench via a hardware-in-the-loop methodology. For camera systems, these benches are called Camera-in-the-Loop (CiL) systems and involve presenting visual stimuli to the device via an external input. In this work, the Mobileye 630 aftermarket monocular camera system was tested across three environments – track based (real life) testing, GoPro CiL bench (recorded real life) testing, and Prescan CiL bench (simulated life) testing. A comparison of the Mobileye outputs was performed to test the response of the camera between the different mediums. Of the two CiL input types, the Prescan environment was found to have the best agreement with the track behavior. While there is potential for both future use and development of CiL testing methodologies, at this time, there are differences which could limit both the applicability and accuracy of the results generated in the bench testing environments, and further exploration is recommended. These differences include reduced detection and FCW distances and variation in response to the same visual input.
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DOI
https://doi.org/10.4271/2023-01-0826
Pages
10
Citation
Bartholomew, M., Midlam-Mohler, S., Guenther, D., Heydinger, G. et al., "Track, GoPro, and Prescan Testing of an ADAS Camera," SAE Int. J. Adv. & Curr. Prac. in Mobility. 5(6):2354-2364, 2023, https://doi.org/10.4271/2023-01-0826.
Additional Details
Publisher
Published
Apr 11, 2023
Product Code
2023-01-0826
Content Type
Journal Article
Language
English