A Cognitive Advanced Driver Assistance Systems Architecture for Autonomous-Capable Electrified Vehicles
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
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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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it