Online Collision Risk Estimation via Monocular Depth-Aware Object Detectors and Fuzzy Inference

This paper presents a monitoring framework that infers the level of autonomous vehicle (AV) collision risk based on its object detector's performance using only monocular camera images. Essentially, the framework takes two sets of predictions produced by different algorithms and associates their inconsistencies with the collision risk via fuzzy inference. The first set of predictions is obtained through retrieving safety-critical 2.5D objects from a depth map, and the second set comes from the AV's 3D object detector. We experimentally validate that, based on Intersection-over-Union (IoU) and a depth discrepancy measure, the inconsistencies between the two sets of predictions strongly correlate to the safety-related error of the 3D object detector against ground truths. This correlation allows us to construct a fuzzy inference system and map the inconsistency measures to an existing collision risk indicator. In particular, we apply various knowledge- and data-driven techniques and find using particle swarm optimization that learns general fuzzy rules gives the best mapping result. Lastly, we validate our monitor's capability to produce relevant risk estimates with the large-scale nuScenes dataset and show it can safeguard an AV in closed-loop simulations.

EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union

This paper presents safety-oriented object detection via a novel Ego-Centric Intersection-over-Union (EC-IoU) measure, addressing practical concerns when applying state-of-the-art learning-based perception models in safety-critical domains such as autonomous driving. Concretely, we propose a weighting mechanism to refine the widely used IoU measure, allowing it to assign a higher score to a prediction that covers closer points of a ground-truth object from the ego agent's perspective. The proposed EC-IoU measure can be used in typical evaluation processes to select object detectors with higher safety-related performance for downstream tasks. It can also be integrated into common loss functions for model fine-tuning. While geared towards safety, our experiment with the KITTI dataset demonstrates the performance of a model trained on EC-IoU can be better than that of a variant trained on IoU in terms of mean Average Precision as well.

Towards Continuous Assurance Case Creation for ADS with the Evidential Tool Bus

An assurance case has become an integral component for the certification of safety-critical systems. While manually defining assurance case patterns can be not avoided, system-specific instantiations of assurance case patterns are both costly and time-consuming. It becomes especially complex to maintain an assurance case for a system when the requirements of the System-Under-Assurance change, or an assurance claim becomes invalid due to, e.g., degradation of a systems component, as common when deploying learning-enabled components. In this paper, we report on our preliminary experience leveraging the tool integration framework Evidential Tool Bus (ETB) for the construction and continuous maintenance of an assurance case from a predefined assurance case pattern. Specifically, we demonstrate the assurance process on an industrial Automated Valet Parking system from the automotive domain. We present the formalization of the provided assurance case pattern in the ETB processable logical specification language of workflows. Our findings show that ETB is able to create and maintain evidence required for the construction of an assurance case.