Environment Influences on Uncertainty of Object Detection for Automated Driving Systems

Ren, Lei; Yin, Huilin; Wan-cheng, GE; Meng, Qian

doi:10.1109/cisp-bmei48845.2019.8965948

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…Ren et al [22] in their study observe an even greater drop in performance of 27 %. In addition, Reference [22] also shows a performance drop of 8 % in detection performance in the mAP due to reduced illumination on images recorded during night time. Similar results show the investigations in Reference [23].…”

Section: Performance Influencing Meta-informationmentioning

confidence: 78%

“…For example, a Faster RCNN reached a mAP approximately 3 % lower due to adverse weather [21]. Ren et al [22] in their study observe an even greater drop in performance of 27 %. In addition, Reference [22] also shows a performance drop of 8 % in detection performance in the mAP due to reduced illumination on images recorded during night time.…”

Section: Performance Influencing Meta-informationmentioning

confidence: 87%

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Identification and Explanation of Challenging Conditions for Camera-Based Object Detection of Automated Vehicles

Ponn

Kröger

Diermeyer

2020

Sensors

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For a safe market launch of automated vehicles, the risks of the overall system as well as the sub-components must be efficiently identified and evaluated. This also includes camera-based object detection using artificial intelligence algorithms. It is trivial and explainable that due to the principle of the camera, performance depends highly on the environmental conditions and can be poor, for example in heavy fog. However, there are other factors influencing the performance of camera-based object detection, which will be comprehensively investigated for the first time in this paper. Furthermore, a precise modeling of the detection performance and the explanation of individual detection results is not possible due to the artificial intelligence based algorithms used. Therefore, a modeling approach based on the investigated influence factors is proposed and the newly developed SHapley Additive exPlanations (SHAP) approach is adopted to analyze and explain the detection performance of different object detection algorithms. The results show that many influence factors such as the relative rotation of an object towards the camera or the position of an object on the image have basically the same influence on the detection performance regardless of the detection algorithm used. In particular, the revealed weaknesses of the tested object detectors can be used to derive challenging and critical scenarios for the testing and type approval of automated vehicles.

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Section: Performance Influencing Meta-informationmentioning

confidence: 78%