Road Maps and Sensor Integration for the Enhancement of Lane-Keeping Assistants
Bibliographic record
Abstract
Current efforts of vehicle manufacturers and research groups in designing and developing safer Intelligent Transportation Systems have revolved around achieving higher levels of driving automation for on-road vehicles. However, current approaches remain unable to assure safe vehicle autonomy in all conditions. Leveraging the communication between the infrastructure, for instance, the road geometry from high-definition maps, and vehicles could be a key enabler of safer Intelligent Transportation Systems. This combination would increase the overall traffic awareness which could benefit current automation approaches. In this study, a new lane-keeping system integrating information from a road map, satellite receiver, and inertial sensors is presented. Tests driving in complex urban environments showed that the proposed system kept the vehicle centered in the lanes during long satellite outages. This result was accomplished with a novel integration between the inertial and road map where the inertial was calibrated by the Map. The position cross-track accuracy upper and lower bounds, at 95% confidence, were 3 and 1 cm from achieving the control limit level (0.1 m) for Intelligent Transportation Location Based Systems. With these results, this work provides a new contribution to increase the robustness of current lane-keeping assistant approaches.
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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".