MétaCan
Menu
Back to cohort
Record W4383535560 · doi:10.54254/2755-2721/5/20230566

Exploration of the feasibility of steering wheelless cars based on Robotaxi operation data

2023· article· en· W4383535560 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied and Computational Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAutonomous Vehicle Technology and Safety
Canadian institutionsQueen's University
Fundersnot available
KeywordsSteering wheelControl (management)Automotive engineeringCourse (navigation)Computer scienceSelf drivingAeronauticsTransport engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

With the development of autonomous driving technology, higher-level autonomous driving is hopeful to be applied in vehicles, and drivers’ control of the car will also be replaced by intelligent algorithms. At the same time, a question has been raised as to whether the traditional steering wheel can be replaced when advanced autonomous driving becomes commonplace. To this end, a survey on steering wheelless cars was conducted to explore the feasibility of autonomous vehicles without the steering wheel. As a result, the operational data of Waymo and Apollo, two Robotaxi (a self-driving car operated by a ridesharing company) companies in the United States and China, is analyzed. The results show that at this stage, autonomous driving cannot fully control the car, and the driver still needs to take over the vehicle in complex situations. Of course, according to the data, Miles per Intervention (MPI) is gradually rising and is expected to reach a reasonable expectation, so steering wheelless cars still have a certain feasibility in the future.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.041
GPT teacher head0.239
Teacher spread0.199 · 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