Enabler Methodology to Use a Dynamic Simulator to Develop Global Vehicles
Bibliographic record
Abstract
The international car manufacturer Stellantis designs and produces vehicles for fourteen brands. Some brand characteristics satisfy both the European and North American market (e.g., Jeep), while others primarily target one market or the other. Efforts have been made recently to harmonize design and development processes within the global company. The cornerstone of this project is the subjective assessment procedure performed on the dynamic driving simulator, a newly developed technology that reduces time and cost during the development phase of a new car model. The technology can be used prior to the production of physical prototypes, allowing the assessment of handling and dynamic qualities continuously during the design phase as various parameters are changed. The purpose of the current research is to determine the most relevant differences in assessments as they are performed in Italy and in North America (i.e., Canada) and to understand the effect on vehicle design as it pertains to the location of the development processes. It was found that the main discriminating factor among the drivers when performing the assessment of a vehicle in design phase is their sensitivity, or their ability to clearly identify the change in behaviour of a vehicle after the design of a component has been modified (e.g., the dampers). If the driver’s sensitivity is high enough, their next concern is to find a balance between the brand identity research (i.e., which kind of customers are targeted and their expectations towards the driving experience) and the safety of the customer (vehicles that are easier to maneuver for the average driver). Eventually, their individual preferences enter the picture; some people simply find sporty driving more appealing while others prefer a vehicle designed with a focus on better ride characteristics.
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 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".