Hardware and software architecture of intelligent vehicles and road verification in typical traffic scenarios
Bibliographic record
Abstract
Intellectualisation is one of the three reforming technologies in automotive industry, which is now changing the mobility mode and human society. High safety and intelligence are the pre‐requisites for putting self‐driving vehicles into markets. This study presents the hardware and software architecture for intelligent vehicles, as well as their road verification in typical traffic scenarios. The hardware system includes environmental sensors, computing platforms, vehicle actuators, and vehicle platforms, which is able to provide redundant protection against the main controller failure. The software system includes environmental perception module, scene cognition module, decision and control module, human–computer interaction module and public service support module. To evaluate the performance of the developed architecture, the road tests of automated driving system were carried out in two typical traffic scenarios, including: (i) closed road test in Yuanboyuan region; (ii) open road test on Beijing‐Tianjin highway. The real road test shows that the designed hardware and software systems for intelligent vehicles have desirable robustness, which can realise accurate and reliable environment perception, decision‐making and motion control.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".