Ensuring the Compatibility of Autonomous Electric Vehicles Components Through a Formal Approach Based on Interaction Protocols
Why this work is in the frame
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Bibliographic record
Abstract
In the context of automotive applications, complex tasks such as automatic driving of electric vehicles are handled through the composition of several components, each offering a different service. Such component composition is not straightforward and is often subject to bugs that might stem mainly from the incompatibility of services. In other words, in this context, which includes critical services and in which people's life is at stake, detecting and eliminating bugs early at the design stage is crucial and even mandatory. To remedy this issue, we propose in this paper a formal approach for modeling and verifying the reliability of electric self-driving vehicles that are continuously communicating with off-road infrastructures. First, for the modeling phase, SysML language is used to model the system architecture and to specify the connections between its embedded components. Second, we present a formal verification approach based on the extended interface automata formalism to verify the compatibility between the interacting components, and to check whether this set of components achieve their required tasks. This formalism allows to specify component interfaces that exhibit component protocols and system non-functional constraints. The proposed approach permits an algorithmic verification to decide whether a set of components, when assembled together, fulfill compatibility conditions. Results in this paper show, on one hand, that SysML and extended interface automata formalism are relevant to model and capture component features in the context of automotive systems, on the other hand, that our methodology allows to develop autonomous electric vehicle systems correct-by-design, regarding to component compatibility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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