In the foreseeable future, highly automated mobile systems, such as vehicles, robots, UAVs, or trains, will be confronted with difficult situations that require external support. The availability of such external support corresponds to level 4 driving automation and is an essential feature in current robotaxis and automated public transportation. While the first generation of level 4 prototypes relied on safety driver support, commercial systems are gradually moving towards support by teleoperation. Designing teleoperation support for level 4 systems is an end-to-end problem involving two main research and practical challenges, the teleoperation function defining the remote human interface with its scene representation and available control functions, and the real-time communication channel involving wired and wireless segments, which must provide reliable end-to-end data transport. Both challenges are tightly linked, and combined solutions are needed to reach the required safe teleoperation. Solutions can make use of the rich sensing and control system of a level 4 vehicle, which however can only be exploited if the communication channel provides adequate real-time access.

Cooperation among vehicles and mobile robotic systems in industrial environments relies on wireless communication. Future roadmaps envision the integration of high-data-rate sensor streams to enhance the performance of such cooperative systems. However, the state-of-the-art in both 802.11 and cellular communication suffers from prolonged and non-deterministic connection losses, lasting up to several seconds, during roaming. As a result, the reliable transmission of large and latency-critical data in such scenarios becomes impractical. For small control data, this issue has been mitigated using redundant data streams. However, this approach is not viable for perception data streams due to excessive resource demands. This work presents an implementation of mechanisms designed to address this challenge, with a focus on seamless deployment in industrial environments using 802.11 and Ethernet TSN backbones. Our physical model truck demonstrator confirmed the feasibility of reliable sample exchange during roaming for data rates up to 25 Mbit/s without active redundancy.

Industrial automation demands continuous, lowlatency streaming for mobile robots, yet mobility often causes handover delays and connectivity issues. We present a physical testbed that validates a streaming solution using multiconnectivity, fast loss detection, and application-level backward error correction with standard 802.11 hardware and Linux-based systems. Our approach achieves seamless handovers in under 10 ms without redundant transmissions, confirming its feasibility for industrial applications.

While autonomous driving roadmaps envision large data to be exchanged in V2X scenarios, current V2X communication standards only focus on reliable exchange of small objects. The Wireless Reliable Real-Time Protocol (W2RP) addresses this challenge, however, cannot properly handle scenarios where multiple applications share the channel especially as there are predetermined periodic and dynamic retransmissions phases for each data object. If transient error spikes occur, applications will compete for dynamic channel resources, potentially creating critical overload situations that can lead to safety violations. Furthermore, the channel is shared by multiple applications with different criticality, properties and constraints. We propose a two-level hierarchical approach that assigns a fixed budget to each node, with periodic and dynamic transmission parts of a node’s applications being scheduled by the node itself. Prioritization is used to differentiate applications based on their criticality, thereby enabling reliable data exchange for safety-critical applications. The concept was evaluated using an OMNeT++-based simulation of an Automated Valet Parking use case and proved highly effective in enabling safe sample exchange even under varying channel conditions, while offering significantly more efficient resource reservation compared to a static configuration.

Future automated mobility is expected to rely on Cooperative Perception (CP) applications to achieve high degrees of reliable autonomy. CP applications are characterized by the exchange of large sensor data objects, such as camera frames or LIDAR point clouds. The high mobility of nodes in combination with the safety-critical nature of CP data, demands architectures enabling continuous connectivity for reliable transmission of large data objects. This work presents a user-centric architecture combining a cell-free Radio-Access-Network (RAN) with a centralized backbone management. By monitoring multiple available connections through an ultra-lightweight heartbeat-based protocol, the handover procedure can be reduced to a low-latency backbone reconfiguration. Thus, the need for redundant data transmission to achieve loss-free and continuous data transmissions during handover is avoided.

Vehicle-to-everything (V2X) roadmaps envision future applications that require the reliable exchange of large sensor data over a wireless network in real time. Applications include sensor fusion for cooperative perception or remote vehicle control that are subject to stringent real-time and safety constraints. Real-time requirements result from end-to-end latency constraints, while reliability refers to the quest for loss-free sensor data transfer to reach maximum application quality. In wireless networks, both requirements are in conflict, because of the need for error correction. Notably, the established video coding standards are not suitable for this task, as demonstrated in experiments. This article shows that middleware-based backward error correction (BEC) in combination with application controlled selective data transmission is far more effective for this purpose. The mechanisms proposed in this article use application and context knowledge to dynamically adapt the data object volume at high error rates at sustained application resilience. We evaluate popular camera datasets and perception pipelines from the automotive domain and apply two complementary strategies. The results and comparisons show that this approach has great benefits, far beyond the state of the art. It also shows that there is no single strategy that outperforms the other in all use cases.

There is a trend towards communication of larger data objects in wireless vehicle communication. In many cases, communication uses publish-subscribe protocols. Data rate requirements of such protocols are best addressed by wireless multicast protocols, but the existing protocols lack an error protection that is suitable for real-time and safety-critical applications. We present an application-aware protocol that supports the popular DDS (Data Distribution Service) middleware. By exploiting data object deadlines and slack for retransmissions and employing an adaptable, multicast-aware prioritization mechanism, the reliable exchange of large data objects is enabled. The protocol is sufficiently general to be used on top of different communication standards such as 802.11- and cellular-based V2X (Vehicle-to-Everything) technologies. The protocol was implemented in an OMNeT++ simulation model and evaluated against recent state-of-the-art alternatives using parameters and constraints taken from a motivational truck platooning example. Furthermore, the protocol was implemented using an open-source DDS implementation as the basis and tested on a physical wireless demonstrator setup. The evaluation shows that the presented multicast protocol substantially outperforms the alternatives keeping streaming applications operational even under high frame error rates.

With the current trend towards large data volumes, state-of-the-art communication and data management solutions based on the publish/subscribe pattern reach the network limits, hampering the deployment of advanced edge and cloud computing services. A main reason is the established publisher-centric distribution mechanism that over-utilizes network resources. We suggest a network and subscriber-centric adaptive data caching communication scheme supported by dynamic network management and configuration, which has great potential to solve the data distribution challenges. We show that state-of-the-art publish/subscribe middlewares like Data Distribution Service (DDS) can transparently be combined with such software caching.

This paper summarizes the talks of a special session on the IPF 2.0 project, a collaborative German-US research project that leverages self-awareness principles for the self-management of distributed systems of autonomous multiprocessor systems-on-chip (MPSoCs).

Despite expectations in roadmaps, the reliable V2X exchange of large data objects under age constraints remains an open challenge. Earlier work proposed a protocol that improved reliability by an application-level Backward Error Correction (BEC) mechanism that operates on data objects rather than packets. In this paper, we extend the protocol to overlapping data object transmission and investigate how far we can increase reliability by relaxing the object transmission deadline to realistic latency requirements as found in the literature. The extended protocol can be used for reliable coupling of nodes and wired network segments in the automotive and industrial domain that exchange large data. As an example, a camera stream transmission from truck platooning is used that aims at improving effectiveness of logistics as part of a large-scale industrial process. We present an analytical model for the extended error correction protocol and evaluate the reliability improvement with an OMNeT++ simulation model that incorporates a state-of-the-art burst error model. For realistic application deadlines, the results show a significant reliability improvement especially for burst errors, at constant protocol overhead.

The current roadmaps and surveys for future wireless networking typically focus on communication and networking technologies and use representative applications to derive future network requirements. Such a benchmarking approach, however, does not cover the application integration challenge that arises from the many distributed applications sharing a network infrastructure, each with their individual topology and data structure. The paper addresses V2X networks as an important example. Crucial end-to-end application constraints including real-time and safety encourage a closer look at application interference and systematic integration. This perspective paper proposes a two-layer resource management that divides the problem into an application integration and a network management task. Valet parking with high-resolution infrastructure camera support is elaborated as a use case that overarches vehicle network and wireless network management. Experiments demonstrate the benefits of complementing the current network-centric management by an application-centric integration.

In automotive and industrial real-time software systems, the primary timing constraints relate to cause-effect chains. A cause-effect chain is a sequence of linked tasks and it typically implements the process of reading sensor data, computing algorithms, and driving actuators. The classic timing analysis computes the maximum end-to-end latency of a given cause-effect chain to verify that its end-to-end deadline can be satisfied in all cases. This information is useful but not sufficient in practice: Software is usually evolving and updates may always alter the maximum end-to-end latency. It would be desirable to judge the quality of a software design a priori by quantifying how robust the timing of a given cause-effect chain will be in the presence of software updates. In this article, we derive robustness margins which guarantee that if software extensions stay within certain bounds, then the end-to-end deadline of a cause-effect chain can still be satisfied. Robustness margins are also useful to know if the system model has uncertain parameters. A robust system design can tolerate bounded deviations from the nominal system model without violating timing constraints. The results are applicable to both the bounded execution time programming model and the (system-level) logical execution time programming model. In this article, we study both an industrial use case from the automotive industry and analyze synthetically generated experiments with our open-source tool TORO.