Real-Time, On-Road Measurement of Driving Behavior, Engine Parameters and Exhaust Emissions
Bibliographic record
Abstract
Automotive tailpipe emissions are a significant contribution to urban air quality problems.(1) However, it is difficult to quantify the extent of that contribution and to quantify any progress in solving the problem. Emissions inventories are commonly based on vehicle registrations, assumed mileage and a set of emission factors. The emission factors are based on dynamometer testing of selected vehicles undertightly controlled conditions. Actual vehicle operation in any urban area encompasses a wider range of vehicles, operating conditions and ambient conditions. Given the highly tuned nature of current engine management systems, the actual in-use emissions levels can be highly sensitive to non-standard ambient and operating situations.(2,3,4,5) This paper describes an on-board system used to record ambient conditions, driving behavior, vehicle operating parameters, fuel consumption and exhaust emissions. The system uses a laptop computer data acquisition system and a number of add-on sensors, (which include a five-gas analyzer and fast-response lambda sensor). Recorded data files are post-processed to measure values ranging from simple vehicle speed and distance traveled to emission rates in grams per kilometer. In addition, using the vehicle speed trace as input to a vehicle dynamic model the tractive power requirements could be calculated. The paper presents results for a small set of repeated commuting trips to illustrate the capabilities and repeatability of the in-use measurement system. Also included are diagnosis of emission control system anomalies which significantly affected emissions but were not detectable by the driver.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".