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Intelligent Sensors and Control for Vehicle Automation
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Intelligent Sensors and Control for Vehicle Automation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 925

Special Issue Editors

Dr. Shaohua Cui

E-Mail Website
Guest Editor
Department of Architecture and Civil Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Interests: autonomous vehicle control; traffic flow control; joint scheduling of electric buses and recharging
Dr. Lei Chen

E-Mail Website
Guest Editor
RISE Research Institutes of Sweden, Gothenburg, Sweden
Interests: cooperative intelligent transport systems
Special Issues, Collections and Topics in MDPI journals
Dr. Wenqi Lu

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
Interests: intelligent transportation systems; connected and automated vehicle & highway; traffic management and control; machine learning

Special Issue Information

Dear Colleagues,

In the rapidly evolving landscape of vehicle automation, intelligent sensors and sophisticated control systems play crucial roles in enhancing the safety, efficiency, and reliability of autonomous vehicles. This Special Issue, entitled "Intelligent Sensors and Control for Vehicle Automation", invites contributions that explore innovative sensor technologies and control strategies that advance the state of the art in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. It will address challenges such as sensor failures and communication disruptions, and their impact on the functionality and safety of automated vehicles. We are particularly interested in research that investigates how these technologies can mitigate traffic disruptions and improve the overall efficiency of transportation systems.

This Special Issue aims to gather insights into the integration of emerging technologies like AI and data analytics with sensor systems to create robust solutions for vehicle automation. Contributions should emphasize novel approaches in dealing with sensor reliability, enhancing communication systems, and ensuring seamless operation under varying environmental conditions. Papers that discuss the socio-economic impacts of automated vehicle technologies, such as their effect on traffic dynamics and urban mobility, are also welcome.

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

  1. Advanced Sensor Technologies in Vehicle Automation: Studies on the latest developments in sensor design and functionality that enhance automated vehicle performance.
  2. Control Strategies for Autonomous Vehicles: Research focusing on innovative control mechanisms that ensure safety and reliability in the face of sensor and communication failures.
  3. Impact of Sensor and Communication Failures: Analysis of how failures affect vehicle behavior and traffic systems, with strategies for mitigation.
  4. Integration of V2V and V2I Communications: Exploration of how vehicle communication systems can enhance sensor data accuracy and vehicle coordination.
  5. Data-Driven Approaches in Vehicle Automation:  Utilization of big data and AI to improve sensor capabilities and decision-making processes in autonomous vehicles.

This Special Issue will provide a comprehensive platform for researchers and practitioners to present their latest findings and innovations in the field of vehicle automation. By focusing on intelligent sensors and control, the issue seeks to propel forward the capabilities and adoption of automated vehicles in modern transportation systems.

Dr. Shaohua Cui
Dr. Lei Chen
Dr. Wenqi Lu
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors 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 2600 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

  • vehicle automation
  • intelligent sensors
  • V2V/V2I communications
  • sensor reliability
  • autonomous control systems

Published Papers (1 paper)

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Research

18 pages, 5519 KiB  
Article
Cooperative Motion Optimization Based on Risk Degree under Automatic Driving Environment
by Miaomiao Liu, Mingyue Zhu, Minkun Yao, Pengrui Li, Renjing Tang and Hui Deng
Sensors 2024, 24(13), 4275; https://doi.org/10.3390/s24134275 - 1 Jul 2024
Viewed by 399
Abstract
Appropriate traffic cooperation at intersections plays a crucial part in modern intelligent transportation systems. To enhance traffic efficiency at intersections, this paper establishes a cooperative motion optimization strategy that adjusts the trajectories of autonomous vehicles (AVs) based on risk degree. Initially, AVs are [...] Read more.
Appropriate traffic cooperation at intersections plays a crucial part in modern intelligent transportation systems. To enhance traffic efficiency at intersections, this paper establishes a cooperative motion optimization strategy that adjusts the trajectories of autonomous vehicles (AVs) based on risk degree. Initially, AVs are presumed to select any exit lanes, thereby optimizing spatial resources. Trajectories are generated for each possible lane. Subsequently, a motion optimization algorithm predicated on risk degree is introduced, which takes into account the trajectories and motion states of AVs. The risk degree serves to prevent collisions between conflicting AVs. A cooperative motion optimization strategy is then formulated, incorporating car-following behavior, traffic signals, and conflict resolution as constraints. Specifically, the movement of all vehicles at the intersection is modified to achieve safer and more efficient traffic flow. The strategy is validated through a simulation using SUMO. The results indicate a 20.51% and 11.59% improvement in traffic efficiency in two typical scenarios when compared to a First-Come-First-Serve approach. Moreover, numerical experiments reveal significant enhancements in the stability of optimized AV acceleration. Full article
(This article belongs to the Special Issue Intelligent Sensors and Control for Vehicle Automation)
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