🤖 AI Summary
This work addresses the insufficient robustness of autonomous driving perception systems under adverse or adversarial driving conditions, which often leads to detection errors or latency. The authors propose a novel framework for quantifying perceptual sensitivity by integrating model disagreement and inference variability, leveraging an ensemble of five state-of-the-art vision models—YOLOv8, YOLOv9, DETR50, DETR101, and RT-DETR—to systematically evaluate performance degradation in both simulated and real-world scenarios involving low illumination, fog, occlusion, and long-range detection. Innovatively, they introduce a stopping-distance-based perceptual evaluation metric that reveals the compounded negative impact of multiple concurrent factors on perception robustness, thereby establishing a new paradigm for safety validation in autonomous driving systems.
📝 Abstract
The viability of automated driving is heavily dependent on the performance of perception systems to provide real-time accurate and reliable information for robust decision-making and maneuvers. These systems must perform reliably not only under ideal conditions, but also when challenged by natural and adversarial driving factors. Both of these types of interference can lead to perception errors and delays in detection and classification. Hence, it is essential to assess the robustness of the perception systems of automated vehicles (AVs) and explore strategies for making perception more reliable. We approach this problem by evaluating perception performance using predictive sensitivity quantification based on an ensemble of models, capturing model disagreement and inference variability across multiple models, under adverse driving scenarios in both simulated environments and real-world conditions. A notional architecture for assessing perception performance is proposed. A perception assessment criterion is developed based on an AV's stopping distance at a stop sign on varying road surfaces, such as dry and wet asphalt, and vehicle speed. Five state-of-the-art computer vision models are used, including YOLO (v8-v9), DEtection TRansformer (DETR50, DETR101), Real-Time DEtection TRansformer (RT-DETR)in our experiments. Diminished lighting conditions, e.g., resulting from the presence of fog and low sun altitude, have the greatest impact on the performance of the perception models. Additionally, adversarial road conditions such as occlusions of roadway objects increase perception sensitivity and model performance drops when faced with a combination of adversarial road conditions and inclement weather conditions. Also, it is demonstrated that the greater the distance to a roadway object, the greater the impact on perception performance, hence diminished perception robustness.