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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. 

Perception algorithms in autonomous vehicles are vital for the vehicle to understand the semantics of its surroundings, including detection and tracking of objects in the environment. The outputs of these algorithms are in turn used for decision-making in safety-critical scenarios like collision avoidance, and automated emergency braking. Thus, it is crucial to monitor such perception systems at runtime. However, due to the high-level, complex representations of the outputs of perception systems, it is a challenge to test and verify these systems, especially at runtime. In this paper, we present a runtime monitoring tool, PerceMon that can monitor arbitrary specifications in Timed Quality Temporal Logic (TQTL) and its extensions with spatial operators. We integrate the tool with the CARLA autonomous vehicle simulation environment and the ROS middleware platform while monitoring properties on state-of-the-art object detection and tracking algorithms.