Bibliographic record
Abstract
Apollo is a high-performance, flexible architecture developed by Baidu, Kinglong, and a consortium of more than 40 companies for the purpose of accelerating the development, testing, and deployment of Autonomous Vehicles. This article first describes Apollo in terms of the functionality of the system and the interaction between its components, and then analyzes the conceptual architecture through system evolution, control and data flow, and concurrency, while illustrating the impact of the division of developer responsibilities on this. Then will go through a detailed overview of the concrete architecture of Baidu Apollo with working on mapping the source code from the Apollo Github Website to our architecture using the diagram drawing tool Scitools Understand. And divided into five parts, the report concludes with five major parts: the process from mapping code to diagram; concrete architecture explanation; unexpected dependencies discovery; subsystem analysis, and sequence diagrams presented in a more accurate and specific way. Finally, this report will propose a specific feature or enhancement to the current concrete architecture. The impact that this feature or enhancement can have on the non-functional requirements and stakeholders of the system will be explored based on the existing architecture and component interactions. In this regard, this report discusses two specific approaches that can be implemented and compares the two approaches through SAAM analysis to determine the best approach.
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 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".