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Sensing, Optimization, and Navigation on Vehicle Control

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 30334

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Special Issue Editors

Dr. Yuanjian Zhang

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Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: autonomous driving, electric vehicles and intelligent systems; new generation clean propulsion control and optimisation; digital modelling and simulation; intelligent transportation system and artificial intelligence (AI) in engineering practice
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Dr. Jingjing Jiang

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Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: shared control (i.e., human–machine interaction); development of advanced driver assistant system (adas); autonomous vehicles; traffic control
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Dr. Ricardo De Castro

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Department of Mechanical Engineering, University of California, Merced, CA 95343, USA
Interests: vehicle dynamics; vehicle control; electric vehicles; hybrid-electric vehicles; autonomous driving
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Dr. Quan Zhou

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Guest Editor

School of Engineering, The University of Birmingham, Birmingham B15 2TT, UK
Interests: connected and autonomous vehicles; hybrid and electric vehicles; engineering optimization; learning-based control and optimization
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Dr. Ji Li

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Guest Editor

Birmingham CASE Automotive Research and Education Centre, School of Engineering, University of Birmingham, Birmingham B15 2SQ, UK
Interests: energy management; hybrid and electric vehicles; driving behavior; man-machine system
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Special Issue Information

Dear Colleagues,

Vehicle controls systems are control commands directed to vehicle actuators that control steering, throttle, and braking, as well as other related commands to support a safe transition between manual and automatic vehicle control. Additionally, the various parts of sensing, positioning, or motion control and energy optimization are crucial for its operation. Therefore, how sensors/sensing technology, navigation and positioning technology, and energy management optimization can be better applied to vehicle local control is a great challenge.

The aim of this Special Issue is to offer the engineering and scientific community a compendium of the most innovative works related to the different new technologies to achieve this goal: guidance and control, sensing technology, sensor systems, navigation and positioning, digital modelling and simulation, collision avoidance, safety, energy management optimization, new generation clean propulsion control and optimisation, intelligent transportation, etc.

Dr. Yuanjian Zhang
Dr. Jingjing Jiang
Dr. Ricardo De Castro
Dr. Quan Zhou
Dr. Ji Li
Guest Editors

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Keywords

autonomous vehicles
integrated vehicle control
localization
sensor fusion
vehicle guidance
energy optimisation

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Published Papers (13 papers)

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Research

27 pages, 3244 KiB

Open AccessArticle

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

by Jianhua Guo, Yinhang Wang, Liang Chu, Chenguang Bai, Zhuoran Hou and Di Zhao

Sensors 2023, 23(12), 5722; https://doi.org/10.3390/s23125722 - 19 Jun 2023

Cited by 4 | Viewed by 2920

Abstract

Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle’s longitudinal motion commands, thus improving the system’s response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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21 pages, 3109 KiB

Open AccessArticle

Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile

by Billy Peralta, Richard Soria, Orietta Nicolis, Fabrizio Ruggeri, Luis Caro and Andrés Bronfman

Sensors 2023, 23(3), 1440; https://doi.org/10.3390/s23031440 - 28 Jan 2023

Cited by 7 | Viewed by 2900

Abstract

In the last decade, a large amount of data from vehicle location sensors has been generated due to the massification of GPS systems to track them. This is because these sensors usually include multiple variables such as position, speed, angular position of the vehicle, etc., and, furthermore, they are also usually recorded in very short time intervals. On the other hand, routes are often generated so that they do not correspond to reality, due to artifacts such as buildings, bridges, or sensor failures and where, due to the large amount of data, visual analysis of human expert is unable to detect genuinely anomalous routes. The presence of such abnormalities can lead to faulty sensors being detected which may allow sensor replacement to reliably track the vehicle. However, given the reliability of the available sensors, there are very few examples of such anomalies, which can make it difficult to apply supervised learning techniques. In this work we propose the use of unsupervised deep neural network models based on stacked autoencoders to detect anomalous routes in vehicles within Santiago de Chile. The results show that the proposed model is capable of effectively detecting anomalous paths in real data considering validation given by an expert user, reaching a performance of 82.1% on average. As future work, we propose to incorporate the use of Long Short-Term Memory (LSTM) and attention-based networks in order to improve the detection of anomalous trajectories. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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23 pages, 6156 KiB

Open AccessArticle

A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs

by Shibo Li, Liang Chu, Jincheng Hu, Shilin Pu, Jihao Li, Zhuoran Hou and Wen Sun

Sensors 2023, 23(3), 1192; https://doi.org/10.3390/s23031192 - 20 Jan 2023

Cited by 11 | Viewed by 1909

Abstract

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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25 pages, 8665 KiB

Open AccessArticle

An Equivalent Consumption Minimization Strategy for a Parallel Plug-In Hybrid Electric Vehicle Based on an Environmental Perceiver

by Shilin Pu, Liang Chu, Jincheng Hu, Shibo Li and Zhuoran Hou

Sensors 2022, 22(24), 9621; https://doi.org/10.3390/s22249621 - 8 Dec 2022

Cited by 5 | Viewed by 1688

Abstract

An energy management strategy is a key technology used to exploit the energy-saving potential of a plug-in hybrid electric vehicle. This paper proposes the environmental perceiver-based equivalent consumption minimization strategy (EP-ECMS) for parallel plug-in hybrid vehicles. In this method, the traffic characteristic information obtained from the intelligent traffic system is used to guide the adjustment of the equivalence factor, improving the environmental adaptiveness of the equivalent consumption minimization strategy (ECMS). Two main works have been completed. First, a high-accuracy environmental perceiver was developed based on a graph convolutional network (GCN) and attention mechanism to complete the traffic state recognition of all graph regions based on historical information. Moreover, it provides the grade of the corresponding region where the vehicle is located (for the ECMS). Secondly, in the offline process, the search for the optimal equivalent factor is completed by using the Harris hawk optimization algorithm based on the representative working conditions under various grades. Based on the identified traffic grades in the online process, the optimized equivalence factor tables are checked for energy management control. The simulation results show that the improved EP-ECMS can achieve 7.25% energy consumption optimization compared with the traditional ECMS. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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19 pages, 5786 KiB

Open AccessArticle

A Dual Distribution Control Method for Multi-Power Components Energy Output of 4WD Electric Vehicles

by Zhiqi Guo, Liang Chu, Zhuoran Hou, Yinhang Wang, Jincheng Hu and Wen Sun

Sensors 2022, 22(24), 9597; https://doi.org/10.3390/s22249597 - 7 Dec 2022

Cited by 1 | Viewed by 1824

Abstract

Energy management strategies are vitally important to give full play to the energy-saving of the four-wheel drive electric vehicle (4WD EV). The cooperative output of multi-power components is involved in the process of driving and braking energy recovery of 4WD EV. This paper proposes a novel energy management strategy of dual equivalent consumption minimization strategy (D-ECMS) to improve the economy of the vehicle. According to the different driving and braking states of the vehicle, D-ECMS can realize the proportional control of the energy cooperative output among the multi-power components. Under the premise of satisfying the dynamic performance of the vehicle, the operating points of the power components are distributed more in the high-efficiency range, and the economy and driving range of the vehicle are optimized. In order to achieve the effectiveness of D-ECMS, MATLAB/Simulink is used to realize the simulation of the vehicle. Compared with the rule-based strategy, the economy of D-ECMS increased by 4.35%. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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17 pages, 2777 KiB

Open AccessArticle

State of Health Prediction of Lithium-Ion Battery Based on Deep Dilated Convolution

by Pengyu Fu, Liang Chu, Jihao Li, Zhiqi Guo, Jincheng Hu and Zhuoran Hou

Sensors 2022, 22(23), 9435; https://doi.org/10.3390/s22239435 - 2 Dec 2022

Cited by 4 | Viewed by 1705

Abstract

A battery’s charging data include the timing information with respect to the charge. However, the existing State of Health (SOH) prediction methods rarely consider this information. This paper proposes a dilated convolution-based SOH prediction model to verify the influence of charging timing information on SOH prediction results. The model uses holes to fill in the standard convolutional kernel in order to expand the receptive field without adding parameters, thereby obtaining a wider range of charging timing information. Experimental data from six batteries of the same battery type were used to verify the model’s effectiveness under different experimental conditions. The proposed method is able to accurately predict the battery SOH value in any range of voltage input through cross-validation, and the SDE (standard deviation of the error) is at least 0.28% lower than other methods. In addition, the influence of the position and length of the range of input voltage on the model’s prediction ability is studied as well. The results of our analysis show that the proposed method is robust to different sampling positions and different sampling lengths of input data, which solves the problem of the original data being difficult to obtain due to the uncertainty of charging–discharging behaviour in actual operation. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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23 pages, 4364 KiB

Open AccessArticle

SGGformer: Shifted Graph Convolutional Graph-Transformer for Traffic Prediction

by Shilin Pu, Liang Chu, Jincheng Hu, Shibo Li, Jihao Li and Wen Sun

Sensors 2022, 22(22), 9024; https://doi.org/10.3390/s22229024 - 21 Nov 2022

Viewed by 2370

Abstract

Accurate traffic prediction is significant in intelligent cities’ safe and stable development. However, due to the complex spatiotemporal correlation of traffic flow data, establishing an accurate traffic prediction model is still challenging. Aiming to meet the challenge, this paper proposes SGGformer, an advanced traffic grade prediction model which combines a shifted window operation, a multi-channel graph convolution network, and a graph Transformer network. Firstly, the shifted window operation is used for coarsening the time series data, thus, the computational complexity can be reduced. Then, a multi-channel graph convolutional network is adopted to capture and aggregate the spatial correlations of the roads in multiple dimensions. Finally, the improved graph Transformer based on the advanced Transformer model is proposed to extract the long-term temporal correlation of traffic data effectively. The prediction performance is evaluated by using actual traffic datasets, and the test results show that the SGGformer proposed exceeds the state-of-the-art baseline. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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15 pages, 4357 KiB

Open AccessArticle

State-of-Health Prediction Using Transfer Learning and a Multi-Feature Fusion Model

by Pengyu Fu, Liang Chu, Zhuoran Hou, Zhiqi Guo, Yang Lin and Jincheng Hu

Sensors 2022, 22(21), 8530; https://doi.org/10.3390/s22218530 - 5 Nov 2022

Cited by 4 | Viewed by 2004

Abstract

Existing data-driven technology for prediction of state of health (SOH) has insufficient feature extraction capability and limited application scope. To deal with this challenge, this paper proposes a battery SOH prediction model based on multi-feature fusion. The model is based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). The CNN can learn the cycle features in the battery data, the LSTM can learn the aging features of the battery over time, and regression prediction can be made through the full-connection layer (FC). In addition, for the aging differences caused by different battery operating conditions, this paper introduces transfer learning (TL) to improve the prediction effect. Across cycle data of the same battery under 12 different charging conditions, the fusion model in this paper shows higher prediction accuracy than with either LSTM and CNN in isolation, reducing RMSPE by 0.21% and 0.19%, respectively. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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19 pages, 9988 KiB

Open AccessArticle

A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution

by Qunyi Chu, Wen Sun and Yuanjian Zhang

Sensors 2022, 22(21), 8358; https://doi.org/10.3390/s22218358 - 31 Oct 2022

Cited by 1 | Viewed by 2073

Abstract

In the study of braking force distribution of trucks, the accurate estimation of the state parameters of the vehicle is very critical. However, during the braking process, the state parameters of the vehicle present a highly nonlinear relationship that is difficult to estimate accurately and that seriously affects the accuracy of the braking force distribution strategy. To solve this problem, this paper proposes a machine-learning-based state-parameter estimation method to provide a solid data base for the braking force distribution strategy of the vehicle. Firstly, the actual collected complete vehicle information is processed for data; secondly, random forest is applied for the feature screening of data to reduce the data dimensionality; subsequently, the generalized regression neural network (GRNN) model is trained offline, and the vehicle state parameters are estimated online; the estimated parameters are used to implement the four-wheel braking force distribution strategy; finally, the effectiveness of the method is verified by joint simulation using MATLAB/Simulink and TruckSim. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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13 pages, 5679 KiB

Open AccessArticle

How Do Human-Driven Vehicles Avoid Pedestrians in Interactive Environments? A Naturalistic Driving Study

by Shulei Sun, Ziqiang Zhang, Zhiqi Zhang, Pengyi Deng, Kai Tian and Chongfeng Wei

Sensors 2022, 22(20), 7860; https://doi.org/10.3390/s22207860 - 16 Oct 2022

Cited by 4 | Viewed by 2081

Abstract

One of the major challenges for autonomous vehicles (AVs) is how to drive in shared pedestrian environments. AVs cannot make their decisions and behaviour human-like or natural when they encounter pedestrians with different crossing intentions. The main reasons for this are the lack of natural driving data and the unclear rationale of the human-driven vehicle and pedestrian interaction. This paper aims to understand the underlying behaviour mechanisms using data of pedestrian–vehicle interactions from a naturalistic driving study (NDS). A naturalistic driving test platform was established to collect motion data of human-driven vehicles and pedestrians. A manual pedestrian intention judgment system was first developed to judge the pedestrian crossing intention at every moment in the interaction process. A total of 98 single pedestrian crossing events of interest were screened from 1274 pedestrian–vehicle interaction events under naturalistic driving conditions. Several performance metrics with quantitative data, including TTC, subjective judgment on pedestrian crossing intention (SJPCI), pedestrian position and crossing direction, and vehicle speed and deceleration were analyzed and applied to evaluate human-driven vehicles’ yielding behaviour towards pedestrians. The results show how vehicles avoid pedestrians in different interaction scenarios, which are classified based on vehicle deceleration. The behaviour and intention results are needed by future AVs, to enable AVs to avoid pedestrians more naturally, safely, and smoothly. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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23 pages, 11383 KiB

Open AccessArticle

A Dual-Adaptive Equivalent Consumption Minimization Strategy for 4WD Plug-In Hybrid Electric Vehicles

by Jianhua Guo, Zhiqi Guo, Liang Chu, Di Zhao, Jincheng Hu and Zhuoran Hou

Sensors 2022, 22(16), 6256; https://doi.org/10.3390/s22166256 - 20 Aug 2022

Cited by 2 | Viewed by 1868

Abstract

Energy management strategies are vitally important to give full play to energy-saving four-wheel-drive plug-in hybrid electric vehicles (4WD PHEV). This paper proposes a novel dual-adaptive equivalent consumption minimization strategy (DA-ECMS) for the complex multi-energy system in the 4WD PHEV. In this strategy, management of the multi-energy system is optimized by introducing the categories of future driving conditions to adjust the equivalent factors and improving the adaptability and economy of driving conditions. Firstly, a self-organizing neural network (SOM) and grey wolf optimizer (GWO) are adopted to classify the driving condition categories and optimize the multi-dimensional equivalent factors offline. Secondly, SOM is adopted to identify driving condition categories and the multi-dimensional equivalent factors are matched. Finally, the DA-ECMS completes the multi-energy optimization management of the front axle multi-energy sources and the electric driving system and releases the energy-saving potential of the 4WD PHEV. Simulation results show that, compared with the rule-based strategy, the economy in the DA-ECMS is improved by 13.31%. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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22 pages, 11098 KiB

Open AccessArticle

A Data-Driven Approach to State of Health Estimation and Prediction for a Lithium-Ion Battery Pack of Electric Buses Based on Real-World Data

by Nan Xu, Yu Xie, Qiao Liu, Fenglai Yue and Di Zhao

Sensors 2022, 22(15), 5762; https://doi.org/10.3390/s22155762 - 2 Aug 2022

Cited by 14 | Viewed by 2945

Abstract

In the era of big data, using big data to realize the online estimation of battery SOH has become possible. Traditional solutions based on theoretical models cannot take into account driving behavior and complicated environmental factors. In this paper, an approximate SOH degradation model based on real operating data and environmental temperature data of electric vehicles (EVs) collected with a big data platform is proposed. Firstly, the health indicators are extracted from the historical operating data, and the equivalent capacity at 25 °C is obtained based on the capacity–temperature empirical formula and the capacity offset. Then, the attenuation rate during each charging and discharging process is calculated by combining the operating data and the environmental temperature. Finally, the long short-term memory (LSTM) neural network is used to learn the degradation trend of the battery and predict the future decline trend. The test results show that the proposed method has better performance. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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17 pages, 7806 KiB

Open AccessArticle

An Underwater Positioning System for UUVs Based on LiDAR Camera and Inertial Measurement Unit

by Hongbo Yang, Zhizun Xu and Baozhu Jia

Sensors 2022, 22(14), 5418; https://doi.org/10.3390/s22145418 - 20 Jul 2022

Cited by 10 | Viewed by 2938

Abstract

Underwater positioning presents a challenging issue, because of the rapid attenuation of electronic magnetic waves, the disturbances and uncertainties in the environment. Conventional methods usually employed acoustic devices to localize Unmanned Underwater Vehicles (UUVs), which suffer from a slow refresh rate, low resolution, and are susceptible to the environmental noise. In addition, the complex terrain can also degrade the accuracy of the acoustic navigation systems. The applications of underwater positioning methods based on visual sensors are prevented by difficulties of acquiring the depth maps due to the sparse features, the changing illumination condition, and the scattering phenomenon. In the paper, a novel visual-based underwater positioning system is proposed based on a Light Detection and Ranging (LiDAR) camera and an inertial measurement unit. The LiDAR camera, benefiting from the laser scanning techniques, could simultaneously generate the associated depth maps. The inertial sensor would offer information about its altitudes. Through the fusion of the data from multiple sensors, the positions of the UUVs can be predicted. After that, the Bundle Adjustment (BA) method is used to recalculate the rotation matrix and the translation vector to improve the accuracy. The experiments are carried out in a tank to illustrate the effects and accuracy of the investigated method, in which the ultra-wideband (UWB) positioning system is used to provide reference trajectories. It is concluded that the developed positioning system is able to estimate the trajectory of UUVs accurately, whilst being stable and robust. Full article

(This article belongs to the Special Issue Sensing, Optimization, and Navigation on Vehicle Control)

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