6 research outputs found

    A middleware protocol for time-critical wireless communication of large data samples

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    We present a middleware-based protocol that reliably synchronizes large samples consisting of multiple frames efficiently and within application level QoS requirements over a lossy wireless channel. The protocol uses a custom retransmission scheme, exploiting the latency requirements on sample level for frame level scheduling. It can be integrated into the popular DDS middleware. We investigate some technical limits of such a protocol and compare it to existing error protocols in the software stack and in the wireless protocol and combinations thereof. The comparison is based on an Omnet++ simulation using an established wireless channel error model. For evaluation, we take a use case from automated valet parking where infrastructure data provided via a wireless link augments in-vehicle sensor data. The use case respects the related safety requirements. Results show that the application awareness of the presented protocol, significantly improves service availability by transmitting data efficiently in time even under higher frame error rates

    Data-Age Analysis and Optimisation for Cause-Effect Chains in Automotive Control Systems

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    Automotive control systems typically have latency requirements for certain cause-effect chains. When implementing and integrating these systems, these latency requirements must be guaranteed e.g. by applying a worst-case analysis that takes all indeterminism and limited predictability of the timing behaviour into account. In this paper, we address the latency analysis for multi-rate distributed cause-effect chains considering staticpriority preemptive scheduling of offset-synchronised periodic tasks. We particularly focus on data age as one representative of the two most common latency semantics. Our main contribution is an Mixed Integer Linear Program-based optimisation to select design parameters (priorities, task-to-processor mapping, offsets) that minimise the data age. In our experimental evaluation, we apply our method to two real-world automotive use cases

    Anomaly Prediction Based on Machine Learning for Memory-Constrained Devices

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    A middleware protocol for time-critical wireless communication of large data samples

    No full text
    We present a middleware-based protocol that reliably synchronizes large samples consisting of multiple frames efficiently and within application level QoS requirements over a lossy wireless channel. The protocol uses a custom retransmission scheme, exploiting the latency requirements on sample level for frame level scheduling. It can be integrated into the popular DDS middleware. We investigate some technical limits of such a protocol and compare it to existing error protocols in the software stack and in the wireless protocol and combinations thereof. The comparison is based on an Omnet++ simulation using an established wireless channel error model. For evaluation, we take a use case from automated valet parking where infrastructure data provided via a wireless link augments in-vehicle sensor data. The use case respects the related safety requirements. Results show that the application awareness of the presented protocol, significantly improves service availability by transmitting data efficiently in time even under higher frame error rates

    A protocol for reliable real-time wireless communication of large data samples

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    In this paper, we present a middleware protocol, that transmits larger-than-a-frame data samples within application real-time and reliability requirements over a lossy wireless channel. The protocol efficiently deploys an intelligent retransmission control that exploits the extended deadline requirements of a sample for frame-level scheduling. The transmission of such samples is placed in the context of the necessary integration of such external sensor data into the decision-making process of an autonomous vehicle. Therefore, we provide parameterization and access rules for communication links in resource-managed scenarios and lay out how to integrate the protocol into the popular DDS middleware. The performance of the parameterized links to satisfy their reliability requirements is studied and compared to error protection protocols in the current software stack. We base the evaluation on an OMNeT++ simulation, whereby an established wireless error model is used. The use case is placed in the context of collaborative sensing in a valet parking environment, where external infrastructure sensor data augments the autonomous vehicle’s data processing chain via a lossy wireless link. Thereby, the use case adheres to the underlying safety requirements. Results show that if the protocol’s awareness of the application requirements is used for parameterization, guarantees for reliability can be provided that outperform existing solutions

    Engineering and Hardening of Functional Fail-Operational Architectures for Highly Automated Driving

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    Rising automation levels in the automotive domain demand a shift from the fail-safe to the fail-operational paradigm. Fail-operational architectures and behaviors are inherently more complex and thus require special diligence from a safety engineering point of view. In this work, we present how we tailored and applied a methodology that facilitates the design of fail-operational architectures from early design stages on by enabling informed judgment regarding the gradually evolved architecture’s fitness for purpose. The method specifically considers resilience regarding dynamic changes in environmental conditions, including V2X aspects and internal capabilities. In this paper, we summarize our experiences in applying the methodology in a highway pilot case study. Furthermore, we present essential extensions of the methodology for modeling and evaluating the operational design domain
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