Abstract
Pedestrian detection is an important part of the perception system of autonomous vehicles. Foggy and low-light conditions are quite challenging for pedestrian detection, and several models have been proposed to increase the robustness of detections under such challenging conditions. Checking if such a model performs well is largely evaluated by manually inspecting the results of object detection. We propose a monitoring technique that uses Timed Quality Temporal Logic (TQTL) to do differential testing: we first check when an object detector (such as vanilla YOLO) fails to accurately detect pedestrians using a suitable TQTL formula on a sequence of images. We then apply a model specialized to adverse weather conditions to perform object detection on the same image sequence. We use Image-Adaptive YOLO (IA-YOLO) for this purpose. We then check if the new model satisfies the previously failing specifications. Our method shows the feasibility of using such a differential testing approach to measure the improvement in quality of detections when specialized models are used for object detection.
S. Mallick and S. Ghosal—These authors contributed equally to this work.
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Acknowledgement
The authors would like to thank the anonymous reviewers for their feedback. This work was supported by the National Science Foundation through the following grants: CAREER award (SHF-2048094), CNS-1932620, CNS-2039087, FMitF-1837131, CCF-SHF-1932620, the Airbus Institute for Engineering Research, and funding by Toyota R &D and Siemens Corporate Research through the USC Center for Autonomy and AI.
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Mallick, S., Ghosal, S., Balakrishnan, A., Deshmukh, J. (2023). Safety Monitoring for Pedestrian Detection in Adverse Conditions. In: Katsaros, P., Nenzi, L. (eds) Runtime Verification. RV 2023. Lecture Notes in Computer Science, vol 14245. Springer, Cham. https://doi.org/10.1007/978-3-031-44267-4_22
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