Virtual Validation of an Automated Lane-Keeping System with an Extended Operational Design Domain

Abstract

Virtual testing using simulation will play a significant role in future safety validation procedures for automated driving systems, as it provides the needed scalability for executing a scenario-based assessment approach. This article combines multiple essential aspects that are necessary for the virtual validation of such systems. First, a general framework that contains the vital subsystems needed for virtual validation is introduced. Secondly, the interfaces between the subsystems are explored. Additionally, the concept of model fidelities is presented and extended towards all relevant subsystems. For an automated lane-keeping system with two different definitions of an operational design domain, all relevant subsystems are defined and integrated into an overall simulation framework. The resulting difference between both operational design domains is the occurrence of lateral manoeuvres, leading to greater demands of the fidelity of the vehicle dynamics model. The simulation results support the initial assumption that by extending the operation domain, the requirements for all subsystems are subject to adaption. As an essential aspect of harmonising virtual validation frameworks, the article identifies four separate layers and their corresponding parameters. In particular, the tool-specific co-simulation capability layer is critical, as it enables model exchange through consistently defined interfaces and reduces the integration effort. The introduction of this layered architecture for virtual validation frameworks enables further cross-domain collaboration.

Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System

“…Thanks to the complete safety case, SMIRK can be used as a starting point for several avenues of future research. First, the SMIRK MVP enables studies on efficient and effective approaches to conduct safety assurance for ODD extension (Weissensteiner et al, 2021). In this context, SMIRK could be used as a platform to study dynamic safety cases (Denney et al, 2015), i.e., updating the safety case as the system evolves, and reuse of safety evidence for new operational contexts (de la Vara et al, 2019).…”

LiMOX—A Point Cloud Lidar Model Toolbox Based on NVIDIA OptiX Ray Tracing Engine

“…where n ∈ [2,3,4]. In Figure 7, the linear fit is compared to these physically motivated fit functions of root two, three and four.…”

“…The research questions addressed in the present work are whether it is possible to develop ray tracing point cloud sensor models based on a modular or stand-alone software architecture, decoupled from direct integration in environment simulations (monolithic architecture), and to model realistic ray–surface interactions with respect to material properties in the IR spectrum with this approach. For object-list-based sensor models, a modular software architecture and integration into simulation frameworks is feasible [ 2 , 3 ], since the interfaces exist to transport object data, e.g., [ 4 ], while point cloud models need access to the entire 3D geometry and attributes thereof, such as materials associated at the polygon level.…”

Safety of Human–Artificial Intelligence Systems: Applying Safety Science to Analyze Loopholes in Interactions between Human Organizations, Artificial Intelligence, and Individual People

“…This is the example because legal regulation of such work has loopholes that can be advantageous for human organizations but disadvantageous for individual people [16,17]. Also, this is the example because increasing automation technology is being introduced for vehicle driving: for example, Automated Lane-Keeping Systems [18] and Intelligent Speed Assistance Technologies that provide drivers with in-vehicle warnings if they are driving above the speed limit [19].…”