A Systematic Review on User Acceptance of Advanced Driver Assistance Systems (ADAS)
Bibliographic record
Abstract
The use of Advanced Driver Assistance Systems (ADAS) to enhance the safety of vehicle occupants and other road users has recently increased. Most vehicle users are not very familiar with the system. Therefore, the identification of factors affecting the user acceptance of ADAS technologies is important from a road safety perspective as well as for vehicle manufacturers. The systematic review focuses on the user acceptance factors identified by several studies conducted worldwide, along with their methods and models. The PRISMA flow strategy is employed for research identification, screening, eligibility checks, and inclusion in the review. The respective studies are identified through a database search and bibliographic check. After filtering the existing studies using a Systematic Classification Scheme (SCS), only thirteen studies are included. Through the screening of the available studies, fifteen user acceptance factors for ADAS technology are identified. Most of these studies have used existing technology acceptance models and behavioral models to identify user acceptance of ADAS technologies. Surprisingly, some significant factors were identified outside the existing models. Hence, the importance of developing a specific user acceptance model for ADAS technology is highlighted in this study. Since there are numerous ADAS technologies available in modern vehicles, the factors identified in this review will be helpful for future researchers to focus on the influence of each factor on the available ADAS features. Furthermore, vehicle manufacturers can also take these factors into account in their future vehicle designs to enhance user awareness and the acceptance of ADAS technologies in their vehicles.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".