Bibliographic record
Abstract
The potential of intelligent vehicles is enormous.They can reduce accidents, save lives of humans by greatly reducing accidents, and can provide a new mode of transportation for people incapable of driving.Moreover, through mobility-on-demand transportation, autonomy can increase the utilization of vehicles in cities, greatly reducing the amount of land devoted to parking vehicles.To achieve this future, we need intelligent vehicles that are both safe and that people are happy and comfortable using.In this talk, I will discuss our recent work on both issues.We will discuss methods to plan safe motion for vehicles through risk aware planning.We will also discuss how remote human supervisors can be leveraged to improve the safety of an autonomous fleet by taking over control of a vehicle at key moments in time.We will then talk about our research in tailoring the behaviour of a vehicle to specific users or passengers.The key to doing this is a method called active preference learning, whereby we demonstrate different vehicle behaviours to a user, and use their feedback to learn a model of their underlying preferences.This preference model can be used to tailor autonomous system to a particular user, making users more comfortable with autonomy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.899 | 0.888 |
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".