arXiv subjects

Julius Kahle

Publications and source records attributed to Julius Kahle.

5 recordsLinked to original sources

arXiv2026

A Comparison of ROS 2 and AUTOSAR Adaptive Platform Against Industry-Elicited Automotive Middleware Requirements

In software-defined vehicles, automotive middleware plays a fundamental role in enabling efficient communication, integration, and coordination among software components. This paper examines how well two of the currently most popular middleware frameworks, ROS 2 Jazzy and AUTOSAR Adaptive Platform R24-11, meet practical requirements elicited from automotive software engineers at one of the major automotive supplier companies, ZF Group. Our objective is to provide insight into an otherwise difficult-to-obtain industrial perspective and support a clearer understanding of priorities in the development and evaluation of middleware for automotive applications.

cs.SE↗

arXiv2025

Towards Timing Isolation for Mixed-Criticality Communication in Software-Defined Vehicles

As the automotive industry transitions toward centralized Linux-based architectures, ensuring the predictable execution of mixed-criticality applications becomes essential. However, concurrent use of the Linux network stack introduces interference, resulting in unpredictable latency and jitter. To address this challenge, we present a layered software architecture that enforces timing isolation for Ethernet-based data exchange between mixed-criticality applications on Linux-based automotive control units. Our approach integrates traffic prioritization strategies at the middleware layer, the network stack layer, and the hardware layer to achieve isolation across the full software stack. At the middleware layer, we implement a fixed-priority, non-preemptive scheduler to manage publishers of varying criticality. At the network layer, we leverage the express data path (XDP) to route high-priority data directly from the network interface driver into critical application memory, bypassing the standard Linux network stack. At the hardware layer, we dedicate a network interface card (NIC) queue exclusively to real-time traffic. We demonstrate how our architecture performs in a Data Distribution Service (DDS)-based system. Our evaluation shows that the approach leads to consistent and predictable latencies for real-time traffic, even under heavy interference from best-effort applications.

cs.NI↗

arXiv2025

Simulation to Reality: Testbeds and Architectures for Connected and Automated Vehicles

Ensuring the safe and efficient operation of CAVs relies heavily on the software framework used. A software framework needs to ensure real-time properties, reliable communication, and efficient resource utilization. Furthermore, a software framework needs to enable seamless transition between testing stages, from simulation to small-scale to full-scale experiments. In this paper, we survey prominent software frameworks used for in-vehicle and inter-vehicle communication in CAVs. We analyze these frameworks regarding opportunities and challenges, such as their real-time properties and transitioning capabilities. Additionally, we delve into the tooling requirements necessary for addressing the associated challenges. We illustrate the practical implications of these challenges through case studies focusing on critical areas such as perception, motion planning, and control. Furthermore, we identify research gaps in the field, highlighting areas where further investigation is needed to advance the development and deployment of safe and efficient CAV systems.

cs.MA↗

arXiv2025

Graph Coloring to Reduce Computation Time in Prioritized Planning

Distributing computations among agents in large networks reduces computational effort in multi-agent path finding (MAPF). One distribution strategy is prioritized planning (PP). In PP, we couple and prioritize interacting agents to achieve a desired behavior across all agents in the network. We characterize the interaction with a directed acyclic graph (DAG). The computation time for solving MAPF problem using PP is mainly determined through the longest path in this DAG. The longest path depends on the fixed undirected coupling graph and the variable prioritization. The approaches from literature to prioritize agents are numerous and pursue various goals. This article presents an approach for prioritization in PP to reduce the longest path length in the coupling DAG and thus the computation time for MAPF using PP. We prove that this problem can be mapped to a graph-coloring problem, in which the number of colors required corresponds to the longest path length in the coupling DAG. We propose a decentralized graph-coloring algorithm to determine priorities for the agents. We evaluate the approach by applying it to multi-agent motion planning (MAMP) for connected and automated vehicles (CAVs) on roads using, a variant of MAPF.

cs.MA↗

arXiv2025

Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning

Multi-agent path finding (MAPF) in large networks is computationally challenging. An approach for MAPF is prioritized planning (PP), in which agents plan sequentially according to their priority. Albeit a computationally efficient approach for MAPF, the solution quality strongly depends on the prioritization. Most prioritizations rely either on heuristics, which do not generalize well, or iterate to find adequate priorities, which costs computational effort. In this work, we show how agents can compute with multiple prioritizations simultaneously. Our approach is general as it does not rely on domain-specific knowledge. The context of this work is multi-agent motion planning (MAMP) with a receding horizon subject to computation time constraints. MAMP considers the system dynamics in more detail compared to MAPF. In numerical experiments on MAMP, we demonstrate that our approach to prioritization comes close to optimal prioritization and outperforms state-of-the-art methods with only a minor increase in computation time. We show real-time capability in an experiment on a road network with ten vehicles in our Cyber-Physical Mobility Lab.

cs.MA↗