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Electronics
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Real-Time Path Planning Design for Autonomous Driving Vehicles
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Real-Time Path Planning Design for Autonomous Driving Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 536

Special Issue Editors

Dr. Feng Huang

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Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
Interests: autonomous driving; urban navigation; robotics
Dr. Xiwei Bai

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
Interests: visual–inertial navigation system; sensor fusion; autonomous driving
Dr. Penghui Xu

E-Mail
Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
Interests: GNSS; deep learning; sensor fusion; navigation

Special Issue Information

Dear Colleagues,

Real-time path planning is key to making intelligent vehicles safe, efficient, and autonomous. Advancement in autonomous driving technology can enable vehicles to quickly sense their surroundings, anticipate changes, and make safe, reliable navigation choices in busy, unpredictable traffic. Reliable path planning helps vehicles drive smoothly without collisions, save energy, shorten travel time, and improve passenger comfort. It also allows vehicles to work together, perform coordinated movements, and coexist well with humans in smart transportation systems. This Special Issue aims to share the latest research and practical progress in real-time path planning for autonomous vehicles, focusing on both theory and engineering. It emphasizes advances in algorithms, computing speed, environmental sensing, and adaptable control that help autonomous systems work well in changing conditions. Hence, for this Issue, we welcome contributions that bridge the gap between simulation and real-world deployment, explore methods combining optimization techniques and control strategies, and demonstrate scalable solutions applicable to diverse traffic and environmental scenarios.

Topics of interest include, but are not limited to, the following:

  • Real-time trajectory and motion planning algorithms for autonomous vehicles;
  • AI and machine learning methods for decision-making and control;
  • Multi-sensor fusion and environmental perception for navigation;
  • Cooperative and distributed path planning for connected vehicles;
  • Safety assurance and human–machine interaction in path planning.

Dr. Feng Huang
Dr. Xiwei Bai
Dr. Penghui Xu
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. Electronics 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

  • real-time path planning
  • autonomous driving
  • trajectory optimization
  • machine learning
  • multi-sensor fusion
  • cooperative vehicles
  • safety assurance
  • human–machine interaction

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Published Papers (1 paper)

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Research

21 pages, 3516 KB  
Article
Diffusion-Guided Model Predictive Control for Signal Temporal Logic Specifications
by Jonghyuck Choi and Kyunghoon Cho
Electronics 2026, 15(3), 551; https://doi.org/10.3390/electronics15030551 - 27 Jan 2026
Viewed by 308
Abstract
We study control synthesis under Signal Temporal Logic (STL) specifications for driving scenarios where strict rule satisfaction is not always feasible and human experts exhibit context-dependent flexibility. We represent such behavior using robustness slackness—learned rule-wise lower bounds on STL robustness—and introduce sub-goals that [...] Read more.
We study control synthesis under Signal Temporal Logic (STL) specifications for driving scenarios where strict rule satisfaction is not always feasible and human experts exhibit context-dependent flexibility. We represent such behavior using robustness slackness—learned rule-wise lower bounds on STL robustness—and introduce sub-goals that encode intermediate intent in the state/output space (e.g., lane-level waypoints). Prior learning-based MPC–STL methods typically infer slackness with VAE priors and plug it into MPC, but these priors can underrepresent multimodal and rare yet valid expert behaviors and do not explicitly model intermediate intent. We propose a diffusion-guided MPC–STL framework that jointly learns slackness and sub-goals from demonstrations and integrates both into STL-constrained MPC. A conditional diffusion model generates pairs of (rule-wise slackness, sub-goal) conditioned on features from the ego vehicle, surrounding traffic, and road context. At run time, a few denoising steps produce samples for the current situation; slackness values define soft STL margins, while sub-goals shape the MPC objective via a terminal (optionally stage) cost, enabling context-dependent trade-offs between rule relaxation and task completion. In closed-loop simulations on held-out highD track-driving scenarios, our method improves task success and yields more realistic lane-changing behavior compared to imitation-learning baselines and MPC–STL variants using CVAE slackness or strict rule enforcement, while remaining computationally tractable for receding-horizon MPC in our experimental setting. Full article
(This article belongs to the Special Issue Real-Time Path Planning Design for Autonomous Driving Vehicles)
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