Safe Perception -- A Hierarchical Monitor Approach

Our transportation world is rapidly transforming induced by an ever increasing level of autonomy. However, to obtain license of fully automated vehicles for widespread public use, it is necessary to assure safety of the entire system, which is still a challenge. This holds in particular for AI-based perception systems that have to handle a diversity of environmental conditions and road users, and at the same time should robustly detect all safety relevant objects (i.e no detection misses should occur). Yet, limited training and validation data make a proof of fault-free operation hardly achievable, as the perception system might be exposed to new, yet unknown objects or conditions on public roads. Hence, new safety approaches for AI-based perception systems are required. For this reason we propose in this paper a novel hierarchical monitoring approach that is able to validate the object list from a primary perception system, can reliably detect detection misses, and at the same time has a very low false alarm rate.

MTBF Model for AVs -- From Perception Errors to Vehicle-Level Failures

The development of Automated Vehicles (AVs) is progressing quickly and the first robotaxi services are being deployed worldwide. However, to receive authority certification for mass deployment, manufactures need to justify that their AVs operate safer than human drivers. This in turn creates the need to estimate and model the collision rate (failure rate) of an AV taking all possible errors and driving situations into account. In other words, there is the strong demand for comprehensive Mean Time Between Failure (MTBF) models for AVs. In this paper, we will introduce such a generic and scalable model that creates a link between errors in the perception system to vehicle-level failures (collisions). Using this model, we are able to derive requirements for the perception quality based on the desired vehicle-level MTBF or vice versa to obtain an MTBF value given a certain mission profile and perception quality.

Robustness of Object Detectors in Degrading Weather Conditions

State-of-the-art object detection systems for autonomous driving achieve promising results in clear weather conditions. However, such autonomous safety critical systems also need to work in degrading weather conditions, such as rain, fog and snow. Unfortunately, most approaches evaluate only on the KITTI dataset, which consists only of clear weather scenes. In this paper we address this issue and perform one of the most detailed evaluation on single and dual modality architectures on data captured in real weather conditions. We analyse the performance degradation of these architectures in degrading weather conditions. We demonstrate that an object detection architecture performing good in clear weather might not be able to handle degrading weather conditions. We also perform ablation studies on the dual modality architectures and show their limitations.

Precise localization relative to 3D Automated Driving map using the Decentralized Kalman filter with Feedback

This paper represents the novel high precision localization approach for Automated Driving (AD) relative to 3D map. The AD maps are not necessarily flat. Hence, the problem of localization is solved here in 3D. The vehicle motion is modeled as piecewise planner but with vertical curvature which is approximated with clothoids. The localization problem is solved with Decentralized Kalman filter with feedback (DKFF) by fusing all available information. The odometry, visual odometry, GPS, the different sensor and mono camera inputs are fused together to obtain the precise localization relative to map. Polylines and landmarks from the map are dealt in the same way because of the line - point geometrical duality. A set of weak filters are accumulated in the strong tracking approach leading to the precise localization results.