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Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 15560

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Dr. Chao Huang

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Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: human–machine collaborative control; decision making; path planning; fault-tolerant control with the application of automated vehicles
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Dr. Yafei Wang

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Institute of Intelligent Vehicle, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
Interests: vehicle dynamics and control; connected and automated vehicles
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Dr. Peng Hang

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School of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: vehicle dynamics and control; steer-by-wire system; motion control for autonomous vehicles
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Prof. Dr. Zhiqiang Zuo

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Guest Editor

School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: autonomous driving; network security and privacy protection; complex networks and multi-agent systems; robust control; antenna servo system control
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Dr. Bo Leng

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School of Automotive Studies, Tongji University, Shanghai 201804, China
Interests: vehicle state estimation; dynamics control for autonomous vehicles
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Dr. Hailong Huang

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Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: multiagent systems; distributed control; shared control; wireless sensor networks; UAV-based applications: search and rescue; construction automation; surveillance; wireless communications; parcel delivery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Connected and automated vehicles (CAVs) are a transformative technology expected to change and improve the safety and efficiency of mobile vehicles. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a research hot spot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to detect obstacles, localize their positions, navigate, and interact with vehicles in the surrounding dynamic environment. Further, by leveraging computer vision and other sensing methods, these vehicles can be taught to recognize in-cabin human body activities, facial emotions, and even their mental states.

This Special Issue aims to compile recent research and development efforts contributing to advances in sensing and control for CAVs. We also welcome contributions addressing the state of the art in the field and perspectives on future developments and applications. Topics of interest within the scope of this Special Issue include (but are not limited to) the following:

Sensing technologies for environment perception of CAVs;
Sensing technologies for localization and navigation of CAVs;
Sensor fusion and signal processing in CAVs;
Sensing for human behavior recognition in CAVs;
Advanced control algorithms for CAVs;
Control-oriented modeling for CAVs;
Decision making, path planning, and tracking of CAVs;
Human–automation collaboration for CAVs;
Sensing, control, and testing for safety and security of CAVs;
Emergency obstacle avoidance control of CAVs;
Eco-driving control of CAVs;
Active steering control of CAVs;
Fault diagnosis and fault-tolerant control of CAVs;
Sensing and control for multimodal vehicles (e.g., ground, aerial, underwater).

Dr. Chao Huang
Dr. Yafei Wang
Dr. Peng Hang
Prof. Dr. Zhiqiang Zuo
Dr. Bo Leng
Dr. Hailong Huang
Guest Editors

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Keywords

connected and automated vehicle (CAV)
sensing technologies
advanced control algorithms
decision making, path planning, and tracking
human–machine collaboration
emergency obstacle avoidance control, fault diagnosis, and fault-tolerant control

Related Special Issue

Advanced Sensing and Safety Control for Connected and Automated Vehicles in Sensors (12 articles)

Published Papers (11 papers)

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Research

16 pages, 4086 KiB

Open AccessArticle

Generating Edge Cases for Testing Autonomous Vehicles Using Real-World Data

by Dhanoop Karunakaran, Julie Stephany Berrio Perez and Stewart Worrall

Sensors 2024, 24(1), 108; https://doi.org/10.3390/s24010108 - 25 Dec 2023

Viewed by 1102

Abstract

In the past decade, automotive companies have invested significantly in autonomous vehicles (AV), but achieving widespread deployment remains a challenge in part due to the complexities of safety evaluation. Traditional distance-based testing has been shown to be expensive and time-consuming. To address this, experts have proposed scenario-based testing (SBT), which simulates detailed real-world driving scenarios to assess vehicle responses efficiently. This paper introduces a method that builds a parametric representation of a driving scenario using collected driving data. By adopting a data-driven approach, we are then able to generate realistic, concrete scenarios that correspond to high-risk situations. A reinforcement learning technique is used to identify the combination of parameter values that result in the failure of a system under test (SUT). The proposed method generates novel, simulated high-risk scenarios, thereby offering a meaningful and focused assessment of AV systems. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ)

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21 pages, 7293 KiB

Open AccessArticle

A Comprehensive Eco-Driving Strategy for CAVs with Microscopic Traffic Simulation Testing Evaluation

by Ozgenur Kavas-Torris and Levent Guvenc

Sensors 2023, 23(20), 8416; https://doi.org/10.3390/s23208416 - 12 Oct 2023

Cited by 2 | Viewed by 745

Abstract

In this paper, a comprehensive deterministic Eco-Driving strategy for Connected and Autonomous Vehicles (CAVs) is presented. In this setup, multiple driving modes calculate speed profiles that are ideal for their own set of constraints simultaneously to save fuel as much as possible, while a High-Level (HL) controller ensures smooth and safe transitions between the driving modes for Eco-Driving. This Eco-Driving deterministic controller for an ego CAV was equipped with Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) algorithms. This comprehensive Eco-Driving strategy and its individual components were tested by using simulations to quantify the fuel economy performance. Simulation results are used to show that the HL controller ensures significant fuel economy improvement as compared to baseline driving modes with no collisions between the ego CAV and traffic vehicles, while the driving mode of the ego CAV was set correctly under changing constraints. For the microscopic traffic simulations, a 6.41% fuel economy improvement was observed for the CAV that was controlled by this comprehensive Eco-Driving strategy. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ)

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24 pages, 17252 KiB

Open AccessArticle

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm

by Debasish Jana and Satish Nagarajaiah

Sensors 2023, 23(18), 7848; https://doi.org/10.3390/s23187848 - 13 Sep 2023

Cited by 1 | Viewed by 791

Abstract

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix’s elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ)

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18 pages, 9722 KiB

Open AccessArticle

Research on the Prediction Model of Engine Output Torque and Real-Time Estimation of the Road Rolling Resistance Coefficient in Tracked Vehicles

by Weijian Jia, Xixia Liu, Guodong Jia, Chuanqing Zhang and Bin Sun

Sensors 2023, 23(17), 7549; https://doi.org/10.3390/s23177549 - 31 Aug 2023

Viewed by 656

Abstract

Road parameter identification is of great significance for the active safety control of tracked vehicles and the improvement of vehicle driving safety. In this study, a method for establishing a prediction model of the engine output torques in tracked vehicles based on vehicle [...] Read more.

f

was further estimated using the model. First, the driving data from the tracked vehicle were collected and then screened by setting the driving conditions of the tracked vehicle. Then, the mapping relationship between the engine torque

T_{e}

, the engine speed

n_{e}

, and the accelerator pedal position

β

was obtained by a genetic algorithm–backpropagation (GA–BP) neural network algorithm, and an engine output torque prediction model was established. Finally, based on the vehicle longitudinal dynamics model, the recursive least squares (RLS) algorithm was used to estimate the

f

. The experimental results showed that when the driving state of the tracked vehicle satisfied the set driving conditions, the engine output torque prediction model could predict the engine output torque

{\hat{T}}_{e}

in real time based on the changes in the

n_{e}

and

β

, and then the RLS algorithm was used to estimate the road rolling resistance coefficient

\hat{f}

. The average coefficient of determination

R

of the

{\hat{T}}_{e}

was 0.91, and the estimation accuracy of the

\hat{f}

was 98.421%. This method could adequately meet the requirements for engine output torque prediction and real-time estimation of the road rolling resistance coefficient during tracked vehicle driving. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ)

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23 pages, 3387 KiB

Open AccessArticle

Using System Dynamics Approach to Explore the Mode Shift between Automated Vehicles, Conventional Vehicles, and Public Transport in Melbourne, Australia

by Yilun Chen, Peter Stasinopoulos, Nirajan Shiwakoti and Shah Khalid Khan

Sensors 2023, 23(17), 7388; https://doi.org/10.3390/s23177388 - 24 Aug 2023

Cited by 1 | Viewed by 1223

Abstract

With the increasing use of automated vehicles (AVs) in the coming decades, government authorities and private companies must leverage their potential disruption to benefit society. Few studies have considered the impact of AVs towards mode shift by considering a range of factors at the city level, especially in Australia. To address this knowledge gap, we developed a system dynamic (SD)-based model to explore the mode shift between conventional vehicles (CVs), AVs, and public transport (PT) by systematically considering a range of factors, such as road network, vehicle cost, public transport supply, and congestion level. By using Melbourne’s Transport Network as a case study, the model simulates the mode shift among AVs, CVs, and PT modes in the transportation system over 50 years, starting from 2018, with the adoption of AVs beginning in 2025. Inputs such as current traffic, road capacity, public perception, and technological advancement of AVs are used to assess the effects of different policy options on the transport systems. The data source used is from the Victorian Integrated Transport Model (VITM), provided by the Department of Transport and Planning, Melbourne, Australia, data from the existing literature, and authors’ assumptions. To our best knowledge, this is the first time using an SD model to investigate the impacts of AVs on mode shift in the Australian context. The findings suggest that AVs will gradually replace CVs as another primary mode of transportation. However, PT will still play a significant role in the transportation system, accounting for 50% of total trips by person after 2058. Cost is the most critical factor affecting AV adoption rates, followed by road network capacity and awareness programs. This study also identifies the need for future research to investigate the induced demand for travel due to the adoption of AVs and the application of equilibrium constraints to the traffic assignment model to increase model accuracy. These findings can be helpful for policymakers and stakeholders to make informed decisions regarding AV adoption policies and strategies. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ)

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16 pages, 3933 KiB

Open AccessArticle

Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios

by Daniele Vignarca, Stefano Arrigoni and Edoardo Sabbioni

Sensors 2023, 23(15), 6888; https://doi.org/10.3390/s23156888 - 03 Aug 2023

Viewed by 859

Abstract

The recent advancements in Intelligent Transportation Systems (ITS) have revealed significant potential for enhancing traffic management through Advanced Driver Assist Systems (ADASs), with benefits for both safety and environment. This research paper proposes a vehicle localization technique based on Kalman filtering, as accurate positioning of the ego-vehicle is essential for the proper functioning of the Traffic Light Advisor (TLA) system. The aim of the TLA is to calculate the most suitable speed to safely reach and pass the first traffic light in front of the vehicle and subsequently keep that velocity constant to overcome the following traffic light, thus allowing safer and more efficient driving practices, thereby reducing safety risks, and minimizing energy consumption. To overcome Global Positioning Systems (GPS) limitations encountered in urban scenarios, a multi-rate sensor fusion approach based on the Kalman filter with map matching and a simple kinematic one-dimensional model is proposed. The experimental results demonstrate an estimation error below 0.5 m on urban roads with GPS signal loss areas, making it suitable for TLA application. The experimental validation of the Traffic Light Advisor system confirmed the expected benefits with a 40% decrease in energy consumption compared to unassisted driving. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ)

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29 pages, 10245 KiB

Open AccessArticle

Trajectory Tracking Coordinated Control of 4WID-4WIS Electric Vehicle Considering Energy Consumption Economy Based on Pose Sensors

by Yiran Qiao, Xinbo Chen and Zhen Liu

Sensors 2023, 23(12), 5496; https://doi.org/10.3390/s23125496 - 11 Jun 2023

Cited by 3 | Viewed by 1157

Abstract

In order to improve the stability and economy of 4WID-4WIS (four-wheel independent drive—four-wheel independent steering) electric vehicles in trajectory tracking, this paper proposes a trajectory tracking coordinated control strategy considering energy consumption economy. First, a hierarchical chassis coordinated control architecture is designed, which includes target planning layer, and coordinated control layer. Then, the trajectory tracking control is decoupled based on the decentralized control structure. Expert PID and Model Predictive Control (MPC) are employed to realize longitudinal velocity tracking and lateral path tracking, respectively, which calculate generalized forces and moments. In addition, with the objective of optimal overall efficiency, the optimal torque distribution for each wheel is achieved using the Mutant Particle Swarm Optimization (MPSO) algorithm. Additionally, the modified Ackermann theory is used to distribute wheel angles. Finally, the control strategy is simulated and verified using Simulink. Comparing the control results of the average distribution strategy and the wheel load distribution strategy, it can be concluded that the proposed coordinated control not only provides good trajectory tracking but also greatly improves the overall efficiency of the motor operating points, which enhances the energy economy and realizes the multi-objective coordinated control of the chassis. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ)

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17 pages, 3136 KiB

Open AccessArticle

Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles

by Mehreen Mushtaq, Ata Ullah, Humaira Ashraf, N.Z Jhanjhi, Mehedi Masud, Abdulmajeed Alqhatani and Mrim M. Alnfiai

Sensors 2023, 23(11), 5217; https://doi.org/10.3390/s23115217 - 31 May 2023

Cited by 3 | Viewed by 1042

Abstract

The Internet of vehicles (IoVs) is an innovative paradigm which ensures a safe journey by communicating with other vehicles. It involves a basic safety message (BSM) that contains sensitive information in a plain text that can be subverted by an adversary. To reduce such attacks, a pool of pseudonyms is allotted which are changed regularly in different zones or contexts. In base schemes, the BSM is sent to neighbors just by considering their speed. However, this parameter is not enough because network topology is very dynamic and vehicles can change their route at any time. This problem increases pseudonym consumption which ultimately increases communication overhead, increases traceability and has high BSM loss. This paper presents an efficient pseudonym consumption protocol (EPCP) which considers the vehicles in the same direction, and similar estimated location. The BSM is shared only to these relevant vehicles. The performance of the purposed scheme in contrast to base schemes is validated via extensive simulations. The results prove that the proposed EPCP technique outperformed compared to its counterparts in terms of pseudonym consumption, BSM loss rate and achieved traceability. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ)

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15 pages, 6158 KiB

Open AccessArticle

Correction of Error of Airborne Anemometers Caused by Self-Excited Air Turbulence

by Jianqiang Liu, Zhan Zhao, Zhen Fang, Yong Li and Lidong Du

Sensors 2023, 23(9), 4288; https://doi.org/10.3390/s23094288 - 26 Apr 2023

Viewed by 1257

Abstract

An airborne anemometer, which monitors wind on the basis of Meteorological Multi-rotor UAVs (Unmanned Aerial Vehicles), is important for the prevention of catastrophe. However, its performance will be affected by the self-excited air turbulence generated by UAV rotors. In this paper, for the purpose of the correction of an error, we developed a method for the elimination of the influence of air turbulence on wind speed measurement. The corresponding correction model is obtained according to the CFD (Computational Fluid Dynamics) simulation of a six-rotor UAV which is carried out with the sliding grid method and the S-A turbulence model. Then, the model is applied to the developed prototype by adding the angle of attack compensation model of the airborne anemometer. It is shown by the actual application that the airborne anemometer can maintain the original measurement accuracy at different ascent speeds. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ)

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13 pages, 2771 KiB

Open AccessArticle

Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data

by Francisca Rosique, Pedro J. Navarro, Leanne Miller and Eduardo Salas

Sensors 2023, 23(4), 2009; https://doi.org/10.3390/s23042009 - 10 Feb 2023

Cited by 2 | Viewed by 3089

Abstract

The development of autonomous vehicles is becoming increasingly popular and gathering real-world data is considered a valuable task. Many datasets have been published recently in the autonomous vehicle sector, with synthetic datasets gaining particular interest due to availability and cost. For a real implementation and correct evaluation of vehicles at higher levels of autonomy, it is also necessary to consider human interaction, which is precisely something that lacks in existing datasets. In this article the UPCT dataset is presented, a public dataset containing high quality, multimodal data obtained using state-of-the-art sensors and equipment installed onboard the UPCT’s CICar autonomous vehicle. The dataset includes data from a variety of perception sensors including 3D LiDAR, cameras, IMU, GPS, encoders, as well as driver biometric data and driver behaviour questionnaires. In addition to the dataset, the software developed for data synchronisation and processing has been made available. The quality of the dataset was validated using an end-to-end neural network model with multiple inputs to obtain the speed and steering wheel angle and it obtained very promising results. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ)

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20 pages, 12527 KiB

Open AccessArticle

Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for Autonomous Vehicles

by Kwangseok Oh and Jaho Seo

Sensors 2023, 23(1), 405; https://doi.org/10.3390/s23010405 - 30 Dec 2022

Cited by 5 | Viewed by 2478

Abstract

This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ)

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