Real Driving Emissions Regulation: European Methodology to fine tune the EU Real Driving Emissions data evaluation method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This European Commission – JRC Technical Report presents a detailed analysis of a dataset made up of 11 passenger cars which have been emission tested during on-road trips for a total of 79 tests. The data set was mostly produced at JRC; except for 3 vehicles. In the framework of the United Nations Economic Commission for Europe (UNECE), the JRC supports DG-GROW (Internal Market, Industry, Entrepreneurship and SMEs) in order to develop an UNECE Regulation and a Global Technical Regulation (GTR) which should include real driving emissions (RDE) testing provisions for several extra-EU countries starting from Japan and South Korea and possibly including India, China, Canada and United States. As a preliminary input to the Global Real Driving informal working group at UNECE, this Report describes the latest EU-RDE procedure (RDE-4, Regulation EU 2018/1832) with focus on the response given by the RDE data analysis tool (EMROAD version 6.03, designed and maintained by JRC). The data set includes RDE tests expressily designed to cover extended boundary conditions (e.g. for temperature and altitude) and to challenge the requirements on trip dynamics which were laid down in the legislation to define the normal condition of vehicle use. EMROAD succesfully produced, and evaluated against requirements, the entire set of parameters defining the trip validity: trip duration, distance and distance shares, vehicle speed and speed shares, trip dynamics, ambient conditions, elevation gain, trip severity with respect to the WLTP driving cycle (based on CO2), emissions of pollutants and their correction for ambient boundary conditions and for excess of severity. The tool was also used to fine tune the tolerances around the CO2 characteristic curve, an useful feature when assessing the degree of test severity which is considered acceptable by the legislator in a specific country. In addition, EMROAD incorporates the previous RDE-3 package (Regulation EU 2017/1151) so that a comparison between the old and most recent provisions can be done. For instance, it was found that the data set was affected by the different methods used in RDE-3 and RDE-4 to build the moving averaging windows for the evaluation of overall trip dynamics: more RDE tests are valid with the latest RDE-4 method than with the older RDE-3.
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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 | 0.019 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it