Co-Operative Adaptive Cruise Control Design for the McMaster EcoCAR Cadillac Lyriq
Bibliographic record
Abstract
This paper presents the implementation of a cooperative adaptive cruise control algorithm (CACC) designed as part of the EcoCAR EV Challenge. The controller will be implemented in a modified 2023 Cadillac Lyriq with the stock advanced driver assistance system (ADAS) disabled. The key innovation in this system includes a fuzzy logic controller which will determine a weighting factor to generate a desired torque output from two independent PID controllers, maintaining either a time-based distance gap to the lead vehicle, or the dynamic target speed. Cruise control without a lead vehicle is handled by jerk-limited S-curve optimization. The objective of these metrics is to improve driver comfort and increase overall vehicle range while prioritizing driver safety. Performance of this adaptive cruise control (ACC) algorithm is evaluated both in a Software-In-Loop (SIL) environment using simulation software, and Vehicle-In-Loop (VIL) where the algorithm is tested on a closed track. The evaluation is done using standardized drive cycles such as the Highway Fuel Economy Driving Schedule (HWFET) and Federal Test Procedure (FTP). These drive cycles were specifically chosen to expose the system to many different scenarios, allowing for a robust system that can safely react to various driver scenarios.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".