AN APPROACH OF HIGH DEFINITION MAP INFORMATION INTERACTION
Bibliographic record
Abstract
Abstract. High definition (HD) maps play a very important role in the realization of autonomous driving technology. It assists self-driving vehicles to efficiently and safely complete a series of driving decisions and route planning by virtue of having most of the accurate and reliable prior information in the road environment. With the continuous change of technology, there are higher requirements for the accuracy, richness and freshness of the information stored in the HD map, so as to assist the practical application of automatic driving technology. However, current research related to HD maps mainly focuses on static information in the road environment. Since there is a large amount of complex, variable and uncertain dynamic information in the road environment, it can be used as prior knowledge for self-driving to make better decisions. Therefore, the research focus of this paper is on the dynamic information. We propose to use HD map as an information system - high definition map information system (HDMIS) - to assist autonomous driving vehicles. We design the specific content of dynamic information in the HDMIS, and develop an information interaction approach based on the vehicle end of the self-driving car and the HDMIS cloud as the interactive subject of dynamic information. In the experiment, we design and build three types of specific traffic scenarios on the simulation platform, and verify the effectiveness of the interaction approach by using the database to perform information interaction between different ports. The results show that our interaction approach can meet the storage and release of dynamic information by HDMIS to a certain extent, and can provide a large amount of dynamic information for autonomous vehicles to help them complete subsequent driving decisions and planning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".