Vehicle Software Engineering (VSE): Research and Practice
Bibliographic record
Abstract
The Internet of Things (IoT) is shaping the future of the automotive industry. Grounded on the advances in everything from sensors, electronic controllers, artificial intelligence, data analytics, to network connectivity, intelligent connected autonomous vehicles (CAVs) have become the essence in IoT applications. The software in CAVs lies at the core of this digital transformation. Faulty software remains the main reason behind the vast number of safety recalls and reputation damage witnessed recently in the automotive industry. The uniqueness of CAVs originates challenges for vehicle software engineering (VSE) that render traditional models and practical solutions for software development ineffective and inapplicable. Despite the raised necessity to adopt a software engineering model that can handle these challenges, there is a lack of studies recognizing the importance of VSE. This article presents an in-depth and comprehensive analysis to perceive the existing software engineering processes detailing their strengths and limitations in the context of CAVs. It also reviews current practical software solutions, including standards, tools, languages, and research efforts to understand the evolution, trends, and current practice in this article area. This article will enable automakers and software providers to better assess and differentiate among the existing software engineering processes and current practical solutions for vehicle software system development. Hence, they would be able to adopt a VSE model and follow best practices that can better meet their challenging needs.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 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".