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Intelligent Sensors and Control for Vehicle Automation

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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: closed (20 November 2024) | Viewed by 25391

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Special Issue Editors

Dr. Shaohua Cui

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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

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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

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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 Issues, Collections and Topics in MDPI journals

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:

Advanced Sensor Technologies in Vehicle Automation: Studies on the latest developments in sensor design and functionality that enhance automated vehicle performance.
Control Strategies for Autonomous Vehicles: Research focusing on innovative control mechanisms that ensure safety and reliability in the face of sensor and communication failures.
Impact of Sensor and Communication Failures: Analysis of how failures affect vehicle behavior and traffic systems, with strategies for mitigation.
Integration of V2V and V2I Communications: Exploration of how vehicle communication systems can enhance sensor data accuracy and vehicle coordination.
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

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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

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Published Papers (9 papers)

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Research

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18 pages, 2818 KiB

Open AccessArticle

A Two-Stage Location-Allocation Optimization Method for Fixed UAV Nests in Power Inspection Considering Node Failure Scenarios

by Zheng Huang, Hongxing Wang, Yiming Tang, Feng Gao, Biao Du and Jia Wang

Sensors 2025, 25(4), 1089; https://doi.org/10.3390/s25041089 - 12 Feb 2025

Viewed by 692

Abstract

This paper explores the configuration and deployment of UAV nests for power inspection operations, focusing on potential nest failures. It proposes a two-stage location-allocation method. The problem is divided into two subproblems, each modeled as an integer linear programming (ILP) problem. The first subproblem identifies the minimal set of nodes for nest construction using the commercial solver Gurobi. The second subproblem involves UAV nest type selection and task allocation, solved with an ILS-SA heuristic algorithm. A case study in China shows that our method reduces total costs by 33.9% and decreases the number of UAV nests by 32% compared to the current greedy deployment method used by the power grid company. These results demonstrate the effectiveness and practicality of our approach in improving the reliability and cost-efficiency of UAV-based power inspection systems. Full article

(This article belongs to the Special Issue Intelligent Sensors and Control for Vehicle Automation)

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20 pages, 2222 KiB

Open AccessArticle

Dynamic Road Anomaly Detection: Harnessing Smartphone Accelerometer Data with Incremental Concept Drift Detection and Classification

by Imen Ferjani and Suleiman Ali Alsaif

Sensors 2024, 24(24), 8112; https://doi.org/10.3390/s24248112 - 19 Dec 2024

Cited by 1 | Viewed by 921

Abstract

Effective monitoring of road conditions is crucial for ensuring safe and efficient transportation systems. By leveraging the power of crowd-sourced smartphone sensor data, road condition monitoring can be conducted in real-time, providing valuable insights for transportation planners, policymakers, and the general public. Previous studies have primarily focused on the use of pre-trained machine learning models and threshold-based methods for anomaly classification, which may not be suitable for real-world scenarios that require incremental detection and classification. As a result, there is a need for novel approaches that can adapt to changing data environments and perform effective classification without relying on pre-existing training data. This study introduces a novel, real-time road condition monitoring technique harnessing smartphone sensor data, addressing the limitations of pre-trained models that lack adaptability in dynamic environments. A hybrid anomaly detection method, combining unsupervised and supervised learning, is proposed to effectively manage concept drift, demonstrating a significant improvement in accuracy and robustness with a 96% success rate. The findings underscore the potential of incremental learning to enhance model responsiveness and efficiency in distinguishing various road anomalies, offering a promising direction for future transportation safety and resource optimization strategies. Full article

(This article belongs to the Special Issue Intelligent Sensors and Control for Vehicle Automation)

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12 pages, 1157 KiB

Open AccessArticle

Multi-Layered Unsupervised Learning Driven by Signal-to-Noise Ratio-Based Relaying for Vehicular Ad Hoc Network-Supported Intelligent Transport System in eHealth Monitoring

by Ali Nauman, Adeel Iqbal, Tahir Khurshaid and Sung Won Kim

Sensors 2024, 24(20), 6548; https://doi.org/10.3390/s24206548 - 11 Oct 2024

Cited by 1 | Viewed by 1524

Abstract

Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations’ 2030 target. Additionally, this framework’s robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper’s contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring. Full article

(This article belongs to the Special Issue Intelligent Sensors and Control for Vehicle Automation)

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19 pages, 3791 KiB

Open AccessArticle

An Adaptive Vehicle Detection Model for Traffic Surveillance of Highway Tunnels Considering Luminance Intensity

by Yongke Wei, Zimu Zeng, Tingquan He, Shanchuan Yu, Yuchuan Du and Cong Zhao

Sensors 2024, 24(18), 5912; https://doi.org/10.3390/s24185912 - 12 Sep 2024

Cited by 1 | Viewed by 1502

Abstract

Vehicle detection is essential for road traffic surveillance and active safety management. Deep learning methods have recently shown robust feature extraction capabilities and achieved improved detection results. However, vehicle detection models often perform poorly under abnormal lighting conditions, especially in highway tunnels. We proposed an adaptive vehicle detection model that accounts for varying luminance intensities to address this issue. The model categorizes the image data into abnormal and normal luminance scenarios. We employ an improved CycleGAN with edge loss as the adaptive luminance adjustment module for abnormal luminance scenarios. This module adjusts the brightness of the images to a normal level through a generative network. Finally, YOLOv7 is utilized for vehicle detection. The experimental results demonstrate that our adaptive vehicle detection model effectively detects vehicles under abnormal luminance scenarios in highway tunnels. The improved CycleGAN can effectively mitigate edge generation distortion. Under abnormal luminance scenarios, our model achieved a 16.3% improvement in precision, a 1.7% improvement in recall, and a 9.8% improvement in mAP_0.5 compared to the original YOLOv7. Additionally, our adaptive luminance adjustment module is transferable and can enhance the detection accuracy of other vehicle detection models. Full article

(This article belongs to the Special Issue Intelligent Sensors and Control for Vehicle Automation)

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17 pages, 1980 KiB

Open AccessArticle

A Cooperative Optimization Model for Variable Approach Lanes at Signaled Intersections Based on Real-Time Flow

by Zhiqiang Zhu, Mingyue Zhu, Miaomiao Liu, Pengrui Li, Renjing Tang and Xuechi Zhang

Sensors 2024, 24(17), 5701; https://doi.org/10.3390/s24175701 - 2 Sep 2024

Viewed by 1517

Abstract

To resolve the congestion caused by imbalanced traffic at intersections, this paper establishes a model of the average delay deviation with the minimization of the average delay in the approach as the optimization objective. Then, the signal control scheme is further optimized based on the variable approach lanes setting. First, we investigate the threshold conditions for setting the VALs under different flows in a single approach direction. The results show that when the ratio of left-turn traffic exceeds the threshold range of 0.20~0.28, the function of the VALs needs to be changed from straight to left-turn. Then, based on the improved Webster’s formula, an optimal timing method that aims at minimizing the average vehicle delay, minimizing the queue length, and maximizing the capacity, is proposed. Finally, taking the actual Huangke intersection in the Hefei demonstration area as an example, three schemes are compared and analyzed in the case of a VAL at the intersection. The results show that under the cooperative optimization scheme proposed in this paper, the travel time and the efficiency of the intersection could be reduced by 18.7% and 9.9%, respectively, when compared with the original and Webster’s schemes. Full article

(This article belongs to the Special Issue Intelligent Sensors and Control for Vehicle Automation)

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18 pages, 3032 KiB

Open AccessArticle

Investigating the Impacts of Autonomous Vehicles on the Efficiency of Road Network and Traffic Demand: A Case Study of Qingdao, China

by Chunguang Liu, Vladimir Zyryanov, Ivan Topilin, Anastasia Feofilova and Mengru Shao

Sensors 2024, 24(16), 5110; https://doi.org/10.3390/s24165110 - 7 Aug 2024

Cited by 3 | Viewed by 3406

Abstract

Rapid urbanization has led to the development of intelligent transport in China. As active safety technology evolves, the integration of autonomous active safety systems is receiving increasing attention to enable the transition from functional to all-weather intelligent driving. In this process of transformation, the goal of automobile development becomes clear: autonomous vehicles. According to the Report on Development Forecast and Strategic Investment Planning Analysis of China’s autonomous vehicle industry, at present, the development scale of China’s intelligent autonomous vehicles has exceeded market expectations. Considering limited research on utilizing autonomous vehicles to meet the needs of urban transportation (transporting passengers), this study investigates how autonomous vehicles affect traffic demand in specific areas, using traffic modeling. It examines how different penetration rates of autonomous vehicles in various scenarios impact the efficiency of road networks with constant traffic demand. In addition, this study also predicts future changes in commuter traffic demand in selected regions using a constructed NL model. The results aim to simulate the delivery of autonomous vehicles to meet the transportation needs of the region. Full article

(This article belongs to the Special Issue Intelligent Sensors and Control for Vehicle Automation)

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17 pages, 4462 KiB

Open AccessArticle

End-to-End Autonomous Driving Decision Method Based on Improved TD3 Algorithm in Complex Scenarios

by Tao Xu, Zhiwei Meng, Weike Lu and Zhongwen Tong

Sensors 2024, 24(15), 4962; https://doi.org/10.3390/s24154962 - 31 Jul 2024

Cited by 2 | Viewed by 2470

Abstract

The ability to make informed decisions in complex scenarios is crucial for intelligent automotive systems. Traditional expert rules and other methods often fall short in complex contexts. Recently, reinforcement learning has garnered significant attention due to its superior decision-making capabilities. However, there exists the phenomenon of inaccurate target network estimation, which limits its decision-making ability in complex scenarios. This paper mainly focuses on the study of the underestimation phenomenon, and proposes an end-to-end autonomous driving decision-making method based on an improved TD3 algorithm. This method employs a forward camera to capture data. By introducing a new critic network to form a triple-critic structure and combining it with the target maximization operation, the underestimation problem in the TD3 algorithm is solved. Subsequently, the multi-timestep averaging method is used to address the policy instability caused by the new single critic. In addition, this paper uses Carla platform to construct multi-vehicle unprotected left turn and congested lane-center driving scenarios and verifies the algorithm. The results demonstrate that our method surpasses baseline DDPG and TD3 algorithms in aspects such as convergence speed, estimation accuracy, and policy stability. Full article

(This article belongs to the Special Issue Intelligent Sensors and Control for Vehicle Automation)

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18 pages, 5519 KiB

Open AccessArticle

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 1112

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 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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22 pages, 4207 KiB

Open AccessReview

A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions

by Francisco Matos, Jorge Bernardino, João Durães and João Cunha

Sensors 2024, 24(16), 5108; https://doi.org/10.3390/s24165108 - 7 Aug 2024

Cited by 4 | Viewed by 9688

Abstract

Autonomous vehicles (AVs) rely heavily on sensors to perceive their surrounding environment and then make decisions and act on them. However, these sensors have weaknesses, and are prone to failure, resulting in decision errors by vehicle controllers that pose significant challenges to their safe operation. To mitigate sensor failures, it is necessary to understand how they occur and how they affect the vehicle’s behavior so that fault-tolerant and fault-masking strategies can be applied. This survey covers 108 publications and presents an overview of the sensors used in AVs today, categorizes the sensor’s failures that can occur, such as radar interferences, ambiguities detection, or camera image failures, and provides an overview of mitigation strategies such as sensor fusion, redundancy, and sensor calibration. It also provides insights into research areas critical to improving safety in the autonomous vehicle industry, so that new or more in-depth research may emerge. Full article

(This article belongs to the Special Issue Intelligent Sensors and Control for Vehicle Automation)

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