A Cognitive Advanced Driver Assistance Systems Architecture for Autonomous-Capable Electrified Vehicles
Bibliographic record
Abstract
Autonomous vehicle industry is making rapid progress in the development of commercial vehicles with higher levels of autonomy. Although the general advanced driver assistance system (ADAS) architecture is widely discussed, limited details are available about the functionality of the modules and their interactions, backed up by scientific justification. This, in turn, limits the utilization of such architecture for pragmatic implementation. A cognitive ADAS architecture for level 4 autonomous-capable electrified vehicles (EVs) is proposed. Variations for levels 3 and 3.5, which are simply seen to be a combination of 3 and 4, with the primary fallback through a human driver and the secondary through an automated driving system, are also presented. A simulation framework is built for highway driving based on the proposed level 4 architecture for an enhanced Tesla Model S. It was concluded that the autonomous control provided a 23% energy economy increase, on average, compared to a human driver control. Through a detailed sensitivity analysis, the optimal mission/motion planning and energy management in addition to the positive impact on the EV battery, motor, and acceleration/deceleration profiles are considered to contribute to this significant increase in the energy economy of an autonomous-controlled EV.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".