TTCAN from Applications to Products in Automotive Systems
Why this work is in the frame
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Bibliographic record
Abstract
<div class="htmlview paragraph">This paper outlines the results of a study performed to analyze the mission of TTCAN from applications to products for automotive systems.</div> <div class="htmlview paragraph">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.</div> <div class="htmlview paragraph">This paper shall have a look particular on the new standard of TTCAN (time-triggered communication on CAN).</div> <div class="htmlview paragraph">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.</div> <div class="htmlview paragraph">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.</div> <div class="htmlview paragraph">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.</div>
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it