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AVL Webinar: AVL Load matrix - Increase you testing efficiency by reducing test duration and improve test quality

 

Load Matrix™ is a flexible and for all technologies adaptable solution which supports product development by combining many years of experience with several important development domains (like statistics, engineering, material science, chemical engineering and computational science) to ensure the best possible product results by using market and end customer usage relevant driving profiles.

14th Graz Symposium Virtual Vehicle

 

 

At the beginning of September ArchitectECA2030 will be presented at the 14th Graz Symposium Virtual Vehicle in Austria, organized by project partners: Virtual Vehicle Research GmbH and Graz University of technology.


The Graz Symposium Virtual Vehicle 2021 serves as a platform to discuss recent advances in systems integration and virtual validation and its optimal coexistence with physical testing. The event focuses on methods, tools, data, and processes for virtual validation.


More information about the event and registration

 

Contaminations on Lidar Sensor Covers: Performance Degradation Including Fault Detection and Modeling as Potential Applications

Birgit Schlager, Thomas Goelles, Stefan Muckenhuber, Daniel Watzenig

Abstract: Lidar sensors play an essential role in the perception system of automated vehicles. Fault Detection, Isolation, Identification, and Recovery (FDIIR) systems are essential for increasing the reliability of lidar sensors. Knowing the influence of different faults on lidar data is the first crucial step towards fault detection for lidar sensors in automated vehicles. We investigate the influences of sensor cover contaminations on the output data, i.e., on the lidar point cloud and full waveform. Different contamination types were applied (dew, dirt, artificial dirt, foam, water, and oil) and the influence on the output data of the single beam lidar RIEGL LD05-A20 and the automotive mechanically spinning lidar Ouster OS1-64 was evaluated. The LD05-A20 measurements show that dew, artificial dirt, and foam lead to unwanted reflections at the sensor cover. Dew, artificial dirt over the entire transmitter, and foam measurements lead to severe faults, i.e., complete sensor blindness. The OS1-64 measurements also show that dew can lead to almost complete sensor blindness. The results look promising for further studies on fault detection and isolation, since the different contamination types lead to different symptom combinations.

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Brno urban dataset: Winter extension

Adam Ligocki, Ales Jelinek, Ludek Zalud

Abstract.This paper presents our latest extension of the Brno Urban Dataset (BUD), the Winter Extension (WE). The dataset contains data from commonly used sensors in the automotive industry, like four RGB and single IR cameras, three 3D LiDARs, differential RTK GNSS receiver with heading estimation, the IMU and FMCW radar. Data from all sensors are precisely timestamped for future offline interpretation and data fusion. The most significant gain of the dataset is the focus on the winter conditions in snow-covered environments.

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Mobility.E Virtual Symposium

 

 

Join the Mobility.E Virtual Symposium where ArchitectECA2030 will be presented by project Co-coordinator Reiner John from AVL List GmbH.


The Mobility.E Virtual Symposium continues the exchange between stakeholders along the mobility value chain and with non-technical stakeholders in terms of introducing vehicle electrification, connectivity and automation. It complements the ECA2030 conference series, which was hosted most recently in October 2020. The essential harmonisation between electronic components and systems and their application is the objective of the Mobility.E Lighthouse of the ECSEL JU, an inter-project and stakeholder collaboration platform at the core of the event.


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The Java2CSP Debugging Tool Utilizing Constraint Solving and Model-Based Diagnosis Principles

Franz Wotawa, Vlad Andrei Dumitru

Abstract: Localizing faults in programs and repairing them is considered a difficult, time-consuming, but necessary activity of software engineering to assure programs fulfilling their expected behavior during operation. In this paper, we introduce the Java2CSP debugging tool implementing the principles of model-based diagnosis for fault localization, which can be accessed over the internet using an ordinary web browser. Java2CSP makes use of a constraint representation of a program together with a failing test case for reporting debugging candidates. The tool supports a non-object-oriented subset of the programming language Java. Java2CSP is not supposed to be used in any production environment. Instead, the tool has been developed for providing a prototypical implementation of a debugger using constraints. We present the underlying foundations behind Java2CSP, discuss some preliminary results, and show how the tool can also be used for test case generation and other applications.

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Towards Fault Simulation at Mixed Register-Transfer/Gate-Level Models

Endri Kaja, Nicolas Gerlin, Mounika Vaddeboina, Luis Rivas, Sebastian Prebeck, Zhao Han, Keerthikumara Devarajegowda, Wolfgang Ecker

Abstract: Safety-critical designs used in automotive applications need to ensure reliable operations even under hostile operating conditions. As these designs grow in size and complexity, they are facing an increased risk of failure. Consequently, the methods applied to validate the reliability of designs require increasingly more compute resources (e.g., fault simulation time) and manual efforts. Rigorous and highly automated safety analysis methods are needed to cope with this rising complexity. In this paper, we propose a model-based safety analysis flow to enable fault injection at different abstraction levels of a design. The fault simulation is performed at register transfer level (RTL) of a design, in which parts of the design targeted for fault simulation are represented with gate-level granularity. This mixed representation of a design provides a significant rise in fault simulation performance while maintaining the same accuracy as a gate-level fault simulation. To demonstrate the applicability of the proposed approach, various RISC- V based CPU subsystems that are part of automotive SoCs are considered for fault simulation. The experimental results show an increase of 3.5x - 8.4x in the fault simulation performance with substantially less manual effort as all the design activities are automated utilizing a model-driven RTL generation flow.

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Acknowledgement

ArchitectECA2030 has been accepted for funding within (ECSEL JU) in collaboration with the European Union’s H2020 Framework Programs under grant agreement No 877539.

The project will receive an ECSEL JU funding up to 4 M€ completed with national budgets from national funding authorities in Germany, Netherlands, Czech Republic, Austria and Norway.  

Project Facts

Short Name: ArchitectECA2030

Full Name: Trustable architectures with acceptable residual risk for the electric, connected and automated cars

Duration:  01/07/2020- 30/06/2023

Total Costs: ~ € 13,6 Mio.

Consortium: 20 partners from 8 countries

Coordinator: Infineon Technologies AG

Funding

 

Horizon 2020
Horizon 2020

 

    

National Funding

National Funding

 


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