Evaluating Automatic Braking Mechanisms for Reducing Driver Fatigue in Low‐Speed Traffic Conditions: A Systematic Review
Bibliographic record
Abstract
Traffic delays are a common challenge for drivers in large cities worldwide. During these delays, drivers must maintain a safe distance from nearby vehicles while avoiding collisions with pedestrians and motorcyclists. This needs frequently alternate pressing and releasing the brake and accelerator pedals, preserving low speeds. Research indicates that this repetitive action can contribute to driver’s fatigue, which is worse for manual vehicles. Other factors, such as inadequate sleep, prolonged driving, monotonous driving conditions, and heavy workloads, may also induce fatigue, further leading to ignorance of the correct seating posture, which can exacerbate the issue. Studies on driver fatigue and its prevention have been widely conducted by scholars and automotive‐based industries, focusing on two subject matters: (i) driver fatigue detection systems using various technologies and (ii) fatigue prevention techniques incorporating autonomous braking systems for high‐speed and long‐distance driving. This paper focuses on extensively reviewing both subject matters, leading to the best proposed solution that can prevent fatigue from happening during road traffic delays at low‐speed driving, as limited studies were found that can suit the traffic and social environment in developing countries, i.e., Kuala Lumpur, Malaysia. Clearly, the latter subject area focused on incorporating autonomous braking systems in the electronic control unit (ECU) of vehicles, applicable only for high‐end vehicles, thus limiting accessibility. This technology can either reduce the physical effort of pedal pressing or take over the task altogether. The review will examine various causes of fatigue and the existing detection methods, compare the automatic braking solutions’ features, and propose a suitable mechanism that could benefit drivers of all types of vehicles, especially from low‐ to middle‐end vehicles, which addresses the real needs among the affected populations with regard to road traffic delay. The outcome of this review comes in the form of a proposal for mitigating the fatigue issue from happening using a unique technique based on the research gap that is adapted to the targeted environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".