Automotive

Excellence in Safety, Reliability, Time-to-Market and Cost-Efficiency

INCHRON is an active member of the AUTOSAR consortium and has a strong footprint and proven track record of success in the automotive industry.

Auto-Testing Timing in Your CI/CD Pipeline

Many teams use continous build / continous deployment (CI/CD) pipeline in their development workflow. Timing requirements can automatically be checked in the test pipeline. chronVIEW is easily integrated using a Docker container.

Development of Safety-Critical Systems: The Timing Aspect

Timing issues that remain undetected or unresolved in a safety-critical system compromise safety – to an extent and in a way that is rarely predictable. Learn how to master the complexity of the timing aspect and how to perform a comprehensive timing analysis by using the INCHRON Tool-Suite and the Infineon AURIX MCDS on-chip trace feature.

Infineon AURIX: Unlocking the Potential of Multi-Core Timing Analysis

Infineon’s Multi-Core Debug Solution (MCDS) provides non-intrusive, parallel trace output that is a very powerful tool to analyze and debug AURIX(TM)-based real-time systems in full operation. Learn how to maximize its benefit for comprehensive timing analysis.

End-to-End Performance Optimization for Autonomous Driving

High quality and safety-critical requirements, aggressive time to market schedule, and increasing technical and organizational complexity are fundamental constraints for autonomous driving. Using the most proven development methods and tools is the key to success. But which aspects are crucial?

Event Chains in Mixed AUTOSAR Classic and Adaptive Platforms

Systems comprising devices based on AUTOSAR classic and adaptive platforms will soon be present in every major vehicle. End-to-end timing along the event chains will be a critical key design element.

Elektrobit, iSYSTEM and INCHRON have created an integrated solution (including demo) for design, software development and tracing.

Perfection in Real-Time: A Key Enabler for Autonomous Driving

Autonomous driving is the most disruptive innovation in the automotive sector ever. It is going to fundamentally change the way we travel by car. Major technological challenges, however, have yet to be overcome to enable fully autonomous driving.

Simulation of Safety-Critical Automotive Control Systems

Electronic control units (ECUs) for safety-critical systems have been successfully developed for decades. In recent years, ADAS and autonomous driving have resulted in an increasing number of software components and microprocessor cores in such systems.
But what does a methodology look like, that considers different aspects of timing in complex safety-critical systems in an efficient, practical and well-proven way?

Autonomous Driving: A Real-Time Challenge

For today’s advanced driver assistance systems (ADAS), about 50 time critical event chains have to be jointly optimized – all across the system, from sensor to actuator, including embedded control units (ECUs), buses, gateway ECUs, hypervisors, and operating systems – to meet all real-time requirements.

When it comes to fully autonomous driving, well above 1000 time critical event chains have to be jointly optimized. This is not something that could be done manually anymore. This is where the INCHRON Tool-Suite comes in handy, with model-based simulation and trace-based analysis & test.

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AUTOSAR Adaptive Platform

The INCHRON Tool-Suite supports comprehensive online event chain analysis and timing requirements evaluation, including scenarios highly relevant for the AUTOSAR Adaptive Platform, like parallel processing with multi-threading, event synchronization and sensor data fusion.

Event Chain Design for Renesas R-Car Gen3 SoC

The INCHRON Tool-Suite now supports R-Car gen3, Renesas’ third generation R-Car automotive computing platform for the autonomous driving era.

Virtualisation for Safe Software Architectures

In cooperation with iSystem, OpenSynergy and Volkswagen.

In the automotive industry, the trend is towards integration of an increasing number of features, that are being developed by an increasing number of independent tier-2 suppliers, on central controllers. Any change wrt. any feature might negatively impact the whole system, such that changes affecting just a single component may result in the need to perform another full verification run on the whole system …

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The objective of a Volkswagen prototyping project is to reduce the strongly increasing efforts for test and re-certification. Software encapsulation is intended to enable tier-2 suppliers to independently verify their respective software component. A hypervisor provides virtual machines (VMs) with well separated memory regions and run time supervision. Such an architecture provides a high degree of flexibility: Each VM can be developed independently of others. Applications of the same ASIL level can be combined into a single VM. We showcase best practices for the design of a hypervisor based architecture and analyse event chains with the help of tracers.

Architecture Variants of Safety-Critical Real-Time Systems

In cooperation with MethodParkSchaeffler and INCHRON.

More and more embedded systems are considered to be safety-critical and are quite often characterised by a combination of high availability requirements and hard real-time requirements. In the automotive industry, safety features and driver assistance features are both falling into this category. In case real-time requirements are not being paid attention to right from the beginning, costly changes of system / software / hardware architecture will be among the consequences …

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In the joined work, an example for a consistent approach is being discussed, covering safety analysis to functional architecture to technical architecture. Safety-critical event chains are being identified, end-to-end real-time requirements are being assigned, and time budgets are being derived – as early as in the functional architecture. Based upon the given functional architecture, possible technical architecture variants are being developed, the respective real-time behavior is being simulated, and finally the variants are being assessed in a systematic way.

Read our Whitepaper 'Assisted to autonomous driving'

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