applsci-logo

Journal Browser

Journal Browser

Artificial Intelligence for Advancing Connected and Autonomous Vehicles

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 526

Special Issue Editors


E-Mail Website
Guest Editor
School of Engineering, Computing and Mathematics, Oxford Brookes University, Oxford OX3 0BP, UK
Interests: autonomous driving; vehicle dynamics; applications of AI to CAVs; road understanding
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Engineering, Computing and Mathematics, Oxford Brookes University, Oxford OX3 0BP, UK
Interests: autonomous robotics; autonomous driving; neural networks; neuromorphic computing

Special Issue Information

Dear Colleagues,

Connected and Autonomous Vehicles (CAVs) are rapidly advancing from technology proof-of-concept to deployed real-world systems. Driving much of this progress, equally remarkable strides have been achieved in Artificial Intelligence (AI) in recent years. Although in popular conception, AI is often associated with large-scale Deep Learning networks and Large Language Models (LLMs), in fact, the scope of AI used in a CAV context is very broad and ranges from 'classical' logical reasoning and filtering to both large and small end-to-end neural networks. Equally, important supporting technologies like simulation and physics modelling are benefitting from AI advances as much as they are informing the AI themselves. This Special Issue will act as a venue for reporting the latest advances regarding the state of the art in applying AI to CAVs across the remit, from fundamental enabling technologies to demonstrator applications. Submissions are encouraged in areas encompassing, but not limited to, the following:

  • Advances in perception systems;
  • Agent modelling;
  • AI for advanced scene understanding;
  • AI for communications systems in CAVs;
  • AI for physical modelling in autonomous driving;
  • AI for sensor fusion;
  • AI systems integration;
  • CAV control and optimal control using AI;
  • CAVs in unstructured or hazardous environments;
  • Hardware for AI-enabled CAVs;
  • Implementations of ethical systems for CAVs;
  • Localization and SLAM;
  • Real-time and online AI;
  • Real-world implementations and testing;
  • Semi-autonomous and teleoperated CAVs;
  • Simulation and AI;
  • System verification and validation.

Dr. Andrew Bradley
Dr. Alex Rast
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autonomous vehicles
  • mobile robotics
  • real-time AI
  • connected vehicles

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 938 KB  
Article
A Comparative Study on Self-Driving Scenario Code Generation Through Prompt Engineering Based on LLM-Specific Characteristics
by Haneul Yang, Hyoeun Kim and Jonggu Kang
Appl. Sci. 2025, 15(23), 12502; https://doi.org/10.3390/app152312502 - 25 Nov 2025
Viewed by 319
Abstract
Large Language Models (LLMs) demonstrate potential in code generation capabilities, yet their applicability in autonomous vehicle control has not been sufficiently explored. This study verifies whether LLMs can generate executable MATLAB code for software-defined vehicle scenarios, comparing five models: GPT-4, Gemini 2.5 Pro, [...] Read more.
Large Language Models (LLMs) demonstrate potential in code generation capabilities, yet their applicability in autonomous vehicle control has not been sufficiently explored. This study verifies whether LLMs can generate executable MATLAB code for software-defined vehicle scenarios, comparing five models: GPT-4, Gemini 2.5 Pro, Claude Sonnet 4.0, CodeLlama-13B-Instruct, and StarCoder2. Thirteen standardised prompts were applied across three types of scenarios: programming-based driving scenarios, inertial sensor-based simulations, and vehicle parking scenarios. Multiple automated evaluation metrics—BLEU, ROUGE-L, ChrF, Spec-Compliance, and Runtime-Sanity—were used to assess code executability, accuracy, and completeness. The results showed GPT-4 achieved the highest score 0.54 in the parking scenario with an overall average score of 0.27, followed by Gemini 2.5 Pro as 0.26. Commercial models demonstrated over 60% execution success rates across all scenarios, whereas open-source models like CodeLlama and StarCoder2 were limited to under 20%. Furthermore, the parking scenario yielded the lowest average score of 0.19, confirming that complex tasks involving sensor synchronisation and trajectory control represent a common limitation across all models. This study presents a new benchmark for quantitatively evaluating the quality of SDV control code generated by LLMs, empirically demonstrating that prompt design and task complexity critically influence model reliability and real-world applicability. Full article
Show Figures

Figure 1

Back to TopTop