Ensuring the Compatibility of Autonomous Electric Vehicles Components Through a Formal Approach Based on Interaction Protocols
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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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".