Automated Generation of Automotive Open System Architecture Electronic Control Unit Configurations Using Xtend: Watchdog Driver Example
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Automotive Open System Architecture (AUTOSAR) is a system-level standard that is formed by the worldwide partnership of automotive manufacturers and suppliers who are working together to develop a standardized Electrical and Electronic (E/E) framework and architecture for automobiles. The AUTOSAR methodology has two main activities: system configuration and the Electronic Control Unit (ECU) configuration. The system configuration is the mapping of the software components to the ECUs based on the system requirements. The ECU configuration (EC) process is an important part of the ECU software integration and generation. ECU-specific information is extracted from the system configuration description, and all the necessary information for the implementation such as tasks, scheduling, and assignments of the runnables to tasks and configuration of the Basic Software (BSW) modules are performed. The EC process involves configuring every single module of the AUTOSAR. Due to the high complexity and redundancy of this process, it has to be supported by different tool-related editors that can automatically generate source files like *.c and *.h for the configuration. In this paper, we propose a method to automate the EC process for AUTOSAR. We use Module Configuration Templates (MCT) written in <i>Xtend</i> programming language along with a BSW configuration source code generator (BSG) Computer-Aided Design (CAD) tool developed at APAG Elektronik. This tool can extract the configuration parameters and automatically generate the required ECU module configuration. The watchdog module will be used as an example to generate and integrate the EC. This enables the seamless generation of the software configurations from the system-level requirements to the software implementation and therefore ensures consistency, correctness, and cost efficiency and reduces the work done by the developer to generate the configuration.</div></div>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".