Powertrain Hardware In The Loop X Vehicle Hardware In the Loop: How to Optimize Their Use During the Software Development Cycle
Bibliographic record
Abstract
The advance of technology and the demand for new features on vehicles has been challenging the automotive industry to find ways to speed up its development process, while increases the robustness and quality of its products. On this context, the embedded software development for vehicles has been directly impacted. In other words, the number of Electronic Control Units (ECUs) considerably increased on the past few years and the number of lines of code, as well as their complexity, have been exponentially increased. In order to deal with this new reality, besides the automotive test prototypes used during the development, the automotive industry has been using different virtual environment to develop, verify and validate its products. As an example, we can see an increase in the use of different Hardware In The Loop system architectures (HILs) such as Powertrain HIL (with real Engine and Transmission Control Modules - ECM and TCM) and Vehicle HILs (with real ECM, TCM, several other ECUs, and also several electronic components that composes the vehicle). Each of these options has its characteristics and limitations and it is necessary a great effort, time and cost to make them available. On this scenario, it is a challenge to define when each type of environment should be used in order to optimize their uses during the development process. One of the keys to answer this question can be based on the activity that will be performed on these environments. So, this article uses the tests and procedures that should be performed on these environments as start points to evaluate and estimate their representativeness and execution time. After that, it will be done a confrontation with other characteristics as reliability, flexibility, setup time and costs of the three environments Page 1 of 11 (automotive test prototype, Powertrain HIL and Vehicle HIL) in order to analyze their challenges, advantages and difficulties and also providing information to optimize the use of each type of environment listed above.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".