Optimizing Vehicle Structure Architectures for Light Trucks
Bibliographic record
Abstract
Electric Vehicles (EVs) have experienced an incredible fast evolution. In the last few years almost every car manufacturer has presented its own EV prototype or fully functional vehicle and developing dedicated vehicles instead of the classical "General Purpose" concept is becoming more common. Most Electric Light Trucks existing already in the market still adopt the classic powertrain lay-out used in thermal engine vehicles. The EC co-funded OPTIBODY project is developing new modular structure architecture for a European L7e category vehicle focused on safety improvement and exploring the capabilities of modularity applied to safety and reparability. The OPTIBODY vehicle has been designed using a modular structure architecture composed of a chassis, a cabin and several add-ons. The cabin will provide improved levels of comfort, protection and ergonomics to the user and the addons will provide protection in case of frontal, side and rear impact, including also crash compatibility and interaction with vulnerable road users. Europe, U.S.A., Canada, Japan and Australia were targeted for the initial analysis of the electric light vehicle worldwide situation to achieve the objectives of the project. The current light trucks fleet, accidentology and the requirements to be fulfilled by the vehicles were analyzed in the previous regions. The chassis, the cabin and the materials and a modular concept to improve self and partner protection safety. The thermal engine has been removed and substituted by electrical inwheels engines, and the extra space has been use to improve frontal impact and vulnerable road users protection. The requirements for certification of both L7e and low-speed vehicle categories in Europe and North America are very low in terms of safety and there is no mandatory crash test to evaluate neither pedestrian protection nor impact performance. OPTIBODY project has proposed frontal, side, rear and pedestrian impact tests and they have been use as targets to design the OPTIBODY vehicle to improve self and partner protection. Frontal crash test simulations showed an improvement in the cabin integrity and self and partner protection, as well as an improved pedestrian protection due to the extra space available, the use of new materials and the design of the add-on. The OPTIBODY vehicle adhered to the US commitment of Part 581 Zone, improving the crash compatibility of the vehicle. The use of modular architectures and new materials also improved the reparability of the vehicle. The OPTIBODY project is developing a new modular architecture for L7e vehicles that will provide an improvement in self and partner protection and reparability. Modularity has been only considered in this vehicle category and its applicability to other categories should be considered. L7e vehicles in Europe and low speed vehicles in the US have very poor safety requirements for certification. The OPTIBODY project is a good opportunity so show a great improvement in self and partner protection for L7e vehicles and also to explore how electric vehicles can improve the current levels of safety and the benefits of applying modularity to safety and reparability fields
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".