Search arXivSearch

arXiv · 2606.10997

A Companion App for an Autonomous Family Vehicle: Identification of Values for an Autonomous Mobility System

Abstract

In this paper, we present a companion app for an autonomous vehicle aimed at user groups who would normally require an accompanying person to drive them. Two aspects of a companion app are presented in this paper: First, the possibility for a trusted person to track the ride of the person in need of support and second, to put the settings of the vehicle for persons in need of support in the hands of a trusted person. In addition, this article describes the requirements and addressed values and discusses the safety-relevant aspects of such a companion app. We also discuss and identify the values that influence passengers and trusted persons using the companion app. Overall, a companion app can provide new perspectives and opportunities for people in need of support, allowing them to take advantage of the features offered by autonomous vehicles. It enables trusted individuals to configure the vehicle according to the passengers needs. Also such an app can be a mechanism to involve trusted persons in the options given by the vehicle and give them the possibility to adapt the vehicle to the needs of the person in need of support.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Leon Johann Brettin, Tobias Schräder, Kerstin Kuhlmann, Vanessa Schmidt, Markus Maurer. 2026-06-09. A Companion App for an Autonomous Family Vehicle: Identification of Values for an Autonomous Mobility System. https://arxiv.org/abs/2606.10997

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Observability and parameter estimation of a generic model for aggregated distributed energy resources

We propose a novel framework for estimating the parameters of an aggregated distributed energy resources (DER A) model. First, we introduce a rigorous method to determine whether all model parameters are estimable. When they are not, our approach identifies the subset of parameters that can be estimated. The proposed framework offers new insights into the number and specific parameters that can be reliably estimated based on commonly available measurements. It also highlights the limitations of calibrating such models. Second, we introduce a Kalman filtering method to calibrate the DER A model. Since we account for nonlinear effects such as saturation and deadbands, we develop a specific mechanism to handle smoothing functions within the Kalman filter. Specifically, we consider the extended and the unscented Kalman filter. We demonstrate the effectiveness of the proposed framework on a modified IEEE 34-node distribution feeder with inverter- based resources. Our findings align with the North American Electric Reliability Corporation's parameterization guideline and underscore the importance of model calibration in accurately capturing the collective dynamics of distributed energy resources installed on distribution systems.

eess.SY

Salted Fisher Information for Hybrid Systems

Discrete events change how parameter-influence propagates in hybrid systems. Prevailing Fisher information for- mulations assume that sensitivities evolve smoothly according to continuous-time variational equations and therefore neglect the sensitivity updates induced by discrete events. This paper derives a Fisher information matrix formulation compatible with hybrid systems. To do so, we use the saltation matrix, which encodes the first-order transformation of sensitivities induced by discrete events. We call the resulting formulation the salted Fisher information matrix (SFIM). The proposed framework unifies continuous information accumulation during flows with discrete updates at event times. We also show that hybrid persistence of excitation is sufficient for the SFIM to be positive definite

eess.SY

Min-Max Grassmannian Optimization for Online Subspace Tracking

We propose GeRoST (Geometrically Robust Subspace Tracking), an online subspace tracking algorithm that models uncertainty in a subspace using a Grassmannian ball. We derive an exact scalar dual for the worst-case subspace problem, establish conditions for a unique worst-case subspace and a Riemannian gradient, and characterize the minimum radius needed to cover a dimensional extension of the target subspace. Each update uses either a spectral direction computed in a reduced subspace or the gradient of the window reconstruction loss. Our numerical experiments show that GeRoST achieves lower mean post-fault prediction error than GREAT in system identification. In video separation, it achieves higher precision and a better precision--recall balance, as measured by the F$_1$ score, than both GREAT and GRASTA at the reported thresholds, with lower recall and longer runtime.

eess.SY