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Cooperative Perception and Control for Autonomous Vehicles

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

Deadline for manuscript submissions: 15 August 2026 | Viewed by 8730

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Special Issue Editors

Dr. Miaomiao Liu

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Guest Editor

School of Transportation Science and Engineering, Beihang University, Beijing, China
Interests: V2X and safety control; traffic big data; road network risk assessment; traffic policy decision analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Anton Rassõlkin

E-Mail Website
Guest Editor

Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: electrical drives; mechatronics; electric vehicles; electrical machines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of perception technology and the improvement of intelligent transportation systems, the status of intelligent connected vehicle applications in autonomous vehicles (AVs) has been gradually improved. Motion control and cooperative perception are important research fields of AVs, so how to effectively deal with scene complexity, environment variability, traffic dynamics, and game interaction are the main challenges for research.

Therefore, This Special Issue aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in AVs' cooperative perception and control systems.

Potential topics include but are not limited to:

Motion control of AVs;
Cooperative perception method;
Sensor equipment application;
Environmental perception;
Traffic safety control theory;
Modern intelligent science and game theory;
Multi-agent learning;
Avs efficiency;
Cooperative control of Avs.

Dr. Miaomiao Liu
Prof. Dr. Anton Rassõlkin
Guest Editors

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Keywords

motion control of AVs
cooperative perception method
sensor equipment application
environmental perception
traffic safety control theory
modern intelligent science and game theory
multi-agent learning
AVs efficiency
cooperative control of AVs

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Published Papers (5 papers)

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Research

19 pages, 7352 KB

Open AccessArticle

Track-to-Track Fusion for Cooperative Perception Using Collective Perception Messages

by Redge Melroy Castelino, Shrijal Pradhan and Axel Hahn

Sensors 2026, 26(6), 2003; https://doi.org/10.3390/s26062003 - 23 Mar 2026

Viewed by 432

Abstract

Vehicle-to-everything communication grants connected and automated road vehicles the opportunity to share their sensor information such as detected road objects for collective awareness. This paper compares various state fusion strategies within a high-level cooperative perception architecture, focusing on the fusion of object-level information provided in standard Collective Perception Messages. This work compares five track-to-track fusion methods, namely Covariance Intersection, Inverse Covariance Intersection, Adapted Extended Kalman Filter, Adapted Unscented Kalman Filter and Information Matrix Fusion, using a simulation framework built with CARLA and Autoware. The methods are analyzed in a case study to assess their performance under different vehicle maneuvers and varying input information accuracy. The case study highlights trade-offs between fusion strategies and illustrate their behavior in asynchronous multi-agent scenarios. While the analysis is conducted in simulation, the architecture is designed to be extensible, and directions for future development are outlined, including the integration of classification and object confidence fusion modules. Full article

(This article belongs to the Special Issue Cooperative Perception and Control for Autonomous Vehicles)

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14 pages, 3826 KB

Open AccessArticle

Multi-Agent Sensor Fusion Methodology Using Deep Reinforcement Learning: Vehicle Sensors to Localization

by Túlio Oliveira Araújo, Marcio Lobo Netto and João Francisco Justo

Sensors 2026, 26(4), 1105; https://doi.org/10.3390/s26041105 - 8 Feb 2026

Viewed by 648

Abstract

Despite recent major advances in autonomous driving, several challenges remain. Even with modern advanced sensors and processing systems, vehicles are still unable to detect all possible obstacles present in complex urban settings and under diverse environmental conditions. Consequently, numerous studies have investigated artificial intelligence methods to improve vehicle perception capabilities. This paper presents a new methodology using a framework named CarAware, which fuses multiple types of sensor data to predict vehicle positions using Deep Reinforcement Learning (DRL). Unlike traditional DRL applications centered on control, this approach focuses on perception. As a case study, the PPO algorithm was used to train and evaluate the effectiveness of this methodology. Full article

(This article belongs to the Special Issue Cooperative Perception and Control for Autonomous Vehicles)

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18 pages, 1241 KB

Open AccessArticle

Performance Evaluation of Cooperative Driving Automation Services Enabled by Edge Roadside Units

by Un-Seon Jung and Cheol Mun

Sensors 2026, 26(2), 504; https://doi.org/10.3390/s26020504 - 12 Jan 2026

Viewed by 547

Abstract

Research on Cooperative Driving Automation (CDA) has advanced to overcome the limited perception range of onboard sensors and the difficulty of inferring surrounding vehicles’ intentions by leveraging vehicle-to-everything (V2X) communications. This paper models how an autonomous vehicle receives cooperative sensing and cooperative maneuvering information generated at an edge roadside unit (edge RSU) that integrates roadside units (RSUs) with multi-access edge computing (MEC), and how the vehicle fuses this information with its onboard situational awareness and path-planning modules. We then analyze the performance gains of edge RSU-enabled services across diverse traffic environments. In a highway-merging scenario, simulations show that employing the edge RSU’s sensor sharing service (SSS) reduces collision risk relative to onboard-only baselines. For unsignalized intersections and roundabouts, we further propose a guidance-driven Hybrid Pairing Optimization (HPO) scheme in which the edge RSU aggregates CAV intents/trajectories, resolves spatiotemporal conflicts via lightweight pairing and time window allocation, and broadcasts maneuver guidance through MSCM. Unlike a first-come, first-served (FCFS) policy that serializes passage, HPO injects edge guidance as soft constraints while preserving arrival order fairness, enabling safe concurrent passage opportunities when feasible. Across intersections and roundabouts, HPO improves average speed by up to 192% and traffic throughput by up to 209% compared with FCFS under identical demand in our simulations. Full article

(This article belongs to the Special Issue Cooperative Perception and Control for Autonomous Vehicles)

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19 pages, 4959 KB

Open AccessEditor’s ChoiceArticle

Performance Optimization of a High-Speed Permanent Magnet Synchronous Motor Drive System for Formula Electric Vehicle Application

by Mahmoud Ibrahim, Oskar Järg, Raigo Seppago and Anton Rassõlkin

Sensors 2025, 25(10), 3156; https://doi.org/10.3390/s25103156 - 16 May 2025

Cited by 8 | Viewed by 3907

Abstract

The proliferation of electric vehicle (EV) racing competitions, such as Formula electric vehicle (FEV) competitions, has intensified the quest for high-performance electric propulsion systems. High-speed permanent magnet synchronous motors (PMSMs) for FEVs necessitate an optimized control strategy that adeptly manages the complex interplay between electromagnetic torque production and minimal power loss, ensuring peak operational efficiency and performance stability across the full speed range. This paper delves into the optimization of high-speed PMSM, pivotal for its application in FEVs. It begins with a thorough overview of the FEV motor’s basic principles, followed by the derivation of a detailed mathematical model that lays the groundwork for subsequent analyses. Utilizing MATLAB/Simulink, a simulation model of the motor drive system was constructed. The proposed strategy synergizes the principles of maximum torque per ampere (MTPA) with the flux weakening control technique instead of conventional zero direct axis current (ZDAC), aiming to push the boundaries of motor performance while navigating the inherent limitations of high-speed operation. Covariance matrix adaptation evolution strategy (CMA-ES) was deployed to determine the optimal d-q axis current ratio achieving maximum operating torque without overdesign problems. The implementation of the optimized control strategy was rigorously tested on the simulation model, with subsequent validation conducted on a real test bench setup. The outcomes of the proposed technique reveal that the tailored control strategy significantly elevates motor torque performance by almost 22%, marking a pivotal advancement in the domain of high-speed PMSM. Full article

(This article belongs to the Special Issue Cooperative Perception and Control for Autonomous Vehicles)

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22 pages, 2706 KB

Open AccessArticle

Innovative Mining of User Requirements Through Combined Topic Modeling and Sentiment Analysis: An Automotive Case Study

by Yujia Liu, Dong Zhang, Qian Wan and Zhongzhen Lin

Sensors 2025, 25(6), 1731; https://doi.org/10.3390/s25061731 - 11 Mar 2025

Cited by 2 | Viewed by 2333

Abstract

As the automotive industry advances rapidly, user needs are in a constant state of evolution. Driven by advancements in big data, artificial intelligence, and natural language processing, mining user requirements from user-generated content (UGC) on social media has become an effective way to understand these dynamic needs. While existing technologies have progressed in topic identification and sentiment analysis, single-method approaches often face limitations. This study proposes a novel method for user requirement mining based on BERTopic and RoBERTa, combining the strengths of topic modeling and sentiment analysis to provide a more comprehensive analysis of user needs. To validate this approach, UGC data from four major Chinese media platforms were collected. BERTopic was applied for topic extraction and RoBERTa for sentiment analysis, facilitating a linked analysis of user emotions and identified topics. The findings categorize user requirements into four main areas—performance, comfort and experience, price sensitivity, and safety—while also reflecting the increasing relevance of advanced features, such as sensors, powertrain performance, and other technologies. This method enhances user requirement identification by integrating sentiment analysis with topic modeling, offering actionable insights for automotive manufacturers in product optimization and marketing strategies and presenting a scalable approach adaptable across various industries. Full article

(This article belongs to the Special Issue Cooperative Perception and Control for Autonomous Vehicles)

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