Urban Residents’ Willingness to Choose and Pay for ADAS and Autonomous Driving Functions: Comparison of Two Cities in China
Bibliographic record
Abstract
ADAS and autonomous driving are booming. As technologies continue to innovate and mature, whether travelers understand, accept, and buy them will directly impact the technological development, popularization, and profitability of these products. This study analyzes the influence of urban residents’ personal, family, and commuting characteristics on their willingness to choose and pay for ADAS and autonomous driving functions. Using the questionnaire survey data for Jiading and Meishan in China, Logit models are established for willingness to choose, and linear regression models are established for willingness to pay. Although Jiading and Meishan are similar in terms of city size and population, there are some differences in the influencing factors for willingness to choose and pay because of the differences in industrial structure, city culture, and residents’ commuting habits. The results show that significant influencing factors vary for different levels of ADAS and autonomous driving functions. The findings of this research can provide a reference for city authorities, designers, and sellers of ADAS products or autonomous vehicles to identify potential buyers and promote related products.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".