Powertrain Hardware In The Loop X Vehicle Hardware In the Loop: How to Optimize Their Use During the Software Development Cycle
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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.</div></div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".