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Record W2943101503 · doi:10.1155/2019/9468451

Effects of In-Vehicle Navigation on Perceptual Responses and Driving Behaviours of Drivers at Tunnel Entrances: A Naturalistic Driving Study

2019· article· en· W2943101503 on OpenAlexvenueno aff
Xinsha Fu, HE Shi-jian, Jintao Du, Ting Ge

Bibliographic record

VenueJournal of Advanced Transportation · 2019
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsDriving simulatorPerceptionSimulationEngineeringPsychology

Abstract

fetched live from OpenAlex

The perceptual responses and driving behaviours of drivers at tunnel entrances vary, which could cause interference and accidents. This study investigated the effects of in-vehicle navigation on the perceptual responses and driving behaviours and whether these effects are actually valid for safety improvement. For this purpose, a series of naturalistic driving experiments was conducted and a comparative analysis was performed considering two different experiment conditions, control condition and in-vehicle navigation condition. Under each condition, the performances of twenty drivers at seven tunnels were evaluated. The area from 200 m outside the tunnel portal to 200 m inside the tunnel portal was averagely divided into four zones. In each zone, two types of perceptual responses (visual responses and psychological responses) and driving behaviours were analysed using six indicators: number of fixations, average duration of fixations, time interval between continuous R-waves, skin conductance response, speed difference in zones, and maximum deceleration. The results showed that in-vehicle navigation significantly affects the perceptual responses and driving behaviours of drivers, and these effects varied in different zones of the tunnel entrance. Furthermore, in-vehicle navigation was found to be valid for safety improvement because beneficial changes in four of the six indicators proved to be effective at appropriate zones. The remaining two indicators, average duration of fixations and maximum deceleration, were not valid, implying that the difficulty of driving information cognition and driving comfort could not be improved by in-vehicle navigation. Moreover, a negative correlation was discovered between the number of fixations and speed difference in zones. This study provides engineers a new knowledge by extending the quantifiable approaches to the analyses of the effectiveness of the effects of in-vehicle navigation.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.220
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2019
Admission routes1
Has abstractyes

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