A Review of Essential Technologies for Autonomous and Semi-autonomous Articulated Heavy Vehicles
Bibliographic record
Abstract
To increase the safety of articulated heavy vehicles (AHVs), attention has been paid to exploring active vehicle safety systems (AVSSs), e.g., anti-lock braking systems.These active vehicle safety technologies are classified as 'reactive safety systems', designed to react to the current vehicle state.These systems are effective, but do not consider the effect of driver error.The main cause of traffic accidents is linked to human errors.A resolution to the problem is autonomous driving, which removes human factors from the control loop.There will be a transition period, during which most vehicles have some capabilities of autonomous driving.Since the late 1990s, lane departure warning and adaptive cruise control systems have been proposed.These technologies are classified as 'predictive safety systems' (PSSs), considering not only the current vehicle state, but also the predicted vehicle state and environmental hazards.For passenger vehicles, several PSSs have been investigated.These PSSs are featured with semiautonomous driving functions.AHVs represent a 7.5 times higher risk than passenger cars in highway operations.However, much less attention has been paid to exploring the PSSs for AHVs.This paper reviews the current status of essential technologies proposed and examined for autonomous and semi-autonomous AHVs.The pros and cons of the technologies are discussed and analyzed.As a result of the review, future research efforts are identified.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".