TTCAN from Applications to Products in Automotive Systems
Bibliographic record
Abstract
This paper outlines the results of a study performed to analyze the mission of TTCAN from applications to products for automotive systems. As commonly acknowledged communication is one of the key elements for future and even present systems such as an automobile. A dramatically increasing number of busses and gateways even in low- to midrange vehicles is putting significant burden upon the validation scenario as well as the cost. Accordingly, numerous new initiatives have been started worldwide in order to find solutions to this; some of them by the definition of enhanced or new protocols. This paper shall have a look particular on the new standard of TTCAN (time-triggered communication on CAN). This protocol is based on the CAN data link layer as specified in ISO 11898-1 and may use standardized CAN physical layers such as specified in ISO 11898-2 (high-speed transceiver) or in ISO 11898-3 (fault-tolerant low-speed transceiver). This particular property is beneficial when migrating towards time-triggered communication approaches. Furthermore TTCAN provides a mechanism to schedule CAN messages either time-triggered or event-triggered. This feature opens new ways to partition, link and structure systems more efficiently in terms of cost and validation. Examples are implementation of a sensor bus, distributed or split control functionality and increased real-time performance in CAN-based in-vehicle networks without software overhead. This paper will identify automotive applications and approaches that require or benefit from the TTCAN protocol. Strengths and limits of the solution are addressed for the domains of powertrain and safety vehicle dynamics. Partitioning will be proposed with the key advantages and system benefits for the applications. A new implementation of a level2 TTCAN node providing full TTCAN functionality with very low software overhead is presented as well.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.045 |
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".