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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 51208

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Prof. Dr. Angel Valera

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Instituto Universitario de Automática e Informática Industrial (Instituto ai2), Universitat Politècnica de València, 46022 Valencia, Spain
Interests: systems engineering; robots; human–robot collaboration; Industry 4.0; position and force robot control; artificial intelligence; advanced robotics; industrial applications; safety systems
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Prof. Dr. Francisco Valero

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Centro de Investigación en Ingeniería Mecánica, (Edificio 5E), Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
Interests: service robotics; trajectory planning; robotic vehicles
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Special Issue Information

Dear Colleagues,

Nowadays there are more than one billion of motor vehicles (cars, trucks and buses) in the world, and if the actual rate of growth continues (the number doubles every 20years), it could be more than 3 billion vehicles in 2040. Because that, in the last years has emerged an active and important research and industry field of interest: the intelligent vehicles. They can be understood as the vehicles that use technologies such as electronics, computer, communications and automatic control to bring social, environmental and economical benefits.

The intelligent vehicle applications require topics related with a) the position, and kinematic and dynamic state of the vehicle, b) the state of the environment surrounding the vehicle, c) the state of the driver and occupants, c) communication with roadside infrastructure or other vehicles, or d) access to digital maps and satellite data.

The aim of this Special Issue is to get a view of the use of on-board and remote sensors in the intelligent vehicle fields to give the reader a clear picture on the advances that are to come. Welcome topics include, but are not strictly limited to, the following:

Global-Local vehicle positioning:

Vehicle location,
Navigation systems,
Obstacles detection,

Road scene understanding:

precise geometry of the lane/road,
road signals and traffic lights detection,
road weather conditions and visibility
road pavement conditions: bumps and breaks in the pavement detection
traffic conditions and presence of other vehicles and/or vulnerable road users

Driver assistance:

collisions avoidance (backup, rear-end, pedestrian, lane-changing collisions),
parking assist,
lane keeping/changing,
emergency assistance

Driver monitoring:

driver fatigue, distraction, inattention,
abnormal driving detection and/or impaired driving
driver and passenger active safety systems,
passenger evaluation for emergency assistance

Prof. Dr. Angel Valera
Prof. Dr. Francisco Valero
Guest Editors

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On-Board and Remote Sensors in Intelligent Vehicles-2nd Edition in Sensors (1 article)

Published Papers (13 papers)

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Research

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27 pages, 23205 KiB

Open AccessArticle

Position and Attitude Determination in Urban Canyon with Tightly Coupled Sensor Fusion and a Prediction-Based GNSS Cycle Slip Detection Using Low-Cost Instruments

by Bálint Vanek, Márton Farkas and Szabolcs Rózsa

Sensors 2023, 23(4), 2141; https://doi.org/10.3390/s23042141 - 14 Feb 2023

Cited by 7 | Viewed by 2152

Abstract

We present a position and attitude estimation algorithm of moving platforms based on the tightly coupled sensor fusion of low-cost multi baseline GNSS, inertial, magnetic and barometric observations obtained by low-cost sensors and affordable dual-frequency GNSS receivers. The sensor fusion algorithm is realized by an Extended Kalman Filter and estimates the states including GNSS receiver inter-channel biases, integer ambiguities and non-GNSS receiver biases. Tightly coupled sensor fusion increases the reliability of the position and attitude solution in challenging environments such as urban canyons by utilizing the inertial observations in case of GNSS outage. Moreover, GNSS observations can be efficiently used to mitigate IMU sensor drifts. Standard GNSS cycle slips detection methods, such as the application of triple differences or linear combinations such as Melbourne–Wübbena combination and the phase ionospheric residual extended TurboEdit method. However, these techniques are not well suited for the localization in quickly changing environments such as urban canyons. We present a new method of tightly coupled sensor fusion supported by a prediction based cycle slip detection technique, applied to a GNSS setup using three antennas leading to multiple moving baselines on the platform. Thus, not only the GNSS signal properties but also the dynamics of the moving platform are considered in the cycle slip detection. The developed algorithm is tested in an open-sky validation measurement and two sets of measurement in an urban canyon area. The sensor fusion algorithm processes the data sets using the proposed prediction-based cycle slip method, the loss-of-lock indicator-based, and for comparison, the Melbourne–Wübbena and the TurboEdit cycle slip detection methods are also included. The obtained position and attitude estimation results are compared to the internal solution of raw data source GNSS receivers and to the observations of a high-accuracy GNSS/INS unit including a fiber optic gyro. The validation test confirms the proper cycle slip detection in an ideal environment. The more challenging urban canyon test results show the reliability and the accuracy of the proposed method. In the case of the second urban canyon test, the proposed method improved the integer ambiguity resolution success rate by 19% and these results show the lowest horizontal and vertical coordinate distortion in comparison of the linear combination and the loss-of-lock-based cycle slip methods. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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14 pages, 7304 KiB

Open AccessArticle

A Predictive Model of a Driver’s Target Trajectory Based on Estimated Driving Behaviors

by Zhanhong Yan, Bo Yang, Zheng Wang and Kimihiko Nakano

Sensors 2023, 23(3), 1405; https://doi.org/10.3390/s23031405 - 26 Jan 2023

Cited by 2 | Viewed by 2412

Abstract

With the development of automated driving, inferring a driver’s behavior can be a key element for designing an Advanced Driver Assistance System (ADAS). Current research is focused on describing and predicting a driver’s behaviors as labels, e.g., lane shifting, lane keeping, etc., during driving. In our work, we consider that predicting a driver’s behavior can be described as predicting a trajectory the driver may follow in the near future. The target trajectory can be calculated through certain polynomial functions. Via the data set collected by a Driving Simulator experiment covering nine volunteers, we proposed a model based on a deep learning network which is capable of predicting the corresponding coefficients of polynomial functions and then generating the trajectories in the next few seconds. The results also discussed and analyzed some possible factors affecting the prediction error. In conclusion, the model proved to be effective in predicting the target trajectory of a driver. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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17 pages, 4771 KiB

Open AccessArticle

Autonomous Driving Control Based on the Technique of Semantic Segmentation

by Jichiang Tsai, Che-Cheng Chang and Tzu Li

Sensors 2023, 23(2), 895; https://doi.org/10.3390/s23020895 - 12 Jan 2023

Cited by 6 | Viewed by 2499

Abstract

Advanced Driver Assistance Systems (ADAS) are only applied to relatively simple scenarios, such as highways. If there is an emergency while driving, the driver should take control of the car to deal properly with the situation at any time. Obviously, this incurs the uncertainty of safety. Recently, in the literature, several studies have been proposed for the above-mentioned issue via Artificial Intelligence (AI). The achievement is exactly the aim that we look forward to, i.e., the autonomous vehicle. In this paper, we realize the autonomous driving control via Deep Reinforcement Learning (DRL) based on the CARLA (Car Learning to Act) simulator. Specifically, we use the ordinary Red-Green-Blue (RGB) camera and semantic segmentation camera to observe the view in front of the vehicle while driving. Then, the captured information is utilized as the input for different DRL models so as to evaluate the performance, where the DRL models include DDPG (Deep Deterministic Policy Gradient) and RDPG (Recurrent Deterministic Policy Gradient). Moreover, we also design an appropriate reward mechanism for these DRL models to realize efficient autonomous driving control. According to the results, only the RDPG strategies can finish the driving mission with the scenario that does not appear/include in the training scenario, and with the help of the semantic segmentation camera, the RDPG control strategy can further improve its efficiency. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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30 pages, 9897 KiB

Open AccessArticle

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors

by Andres E. Campos-Ferreira, Jorge de J. Lozoya-Santos, Juan C. Tudon-Martinez, Ricardo A. Ramirez Mendoza, Adriana Vargas-Martínez, Ruben Morales-Menendez and Diego Lozano

Sensors 2023, 23(2), 814; https://doi.org/10.3390/s23020814 - 10 Jan 2023

Cited by 13 | Viewed by 7413

Abstract

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver’s health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle’s central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver’s heart condition and vehicular traffic have been found in this analysis. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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15 pages, 6714 KiB

Open AccessArticle

The Application of PVDF-Based Piezoelectric Patches in Energy Harvesting from Tire Deformation

by Kevin Nguyen, Matthew Bryant, In-Hyouk Song, Byoung Hee You and Seyedmeysam Khaleghian

Sensors 2022, 22(24), 9995; https://doi.org/10.3390/s22249995 - 19 Dec 2022

Cited by 10 | Viewed by 2919

Abstract

The application of Polyvinylidene Fluoride or Polyvinylidene Difluoride (PVDF) in harvesting energy from tire deformation was investigated in this study. An instrumented tire with different sizes of PVDF-based piezoelectric patches and a tri-axial accelerometer attached to its inner liner was used for this purpose and was tested under different conditions on asphalt and concrete surfaces. The results demonstrated that on both pavement types, the generated voltage was directly proportional to the size of the harvester patches, the longitudinal velocity, and the normal load. Additionally, the generated voltage was inversely proportional to the tire inflation pressure. Moreover, the range of generated voltages was slightly higher on asphalt compared to the same testing conditions on the concrete surface. Based on the results, it was concluded that in addition to the potential role of the PVDF-based piezoelectric film in harvesting energy from tire deformation, they demonstrate great potential to be used as self-powered sensors to estimate the tire-road contact parameters. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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17 pages, 19175 KiB

Open AccessArticle

An Automatic Incident Detection Method for a Vehicle-to-Infrastructure Communication Environment: Case Study of Interstate 64 in Missouri

by Kun Zhang and Jalil Kianfar

Sensors 2022, 22(23), 9197; https://doi.org/10.3390/s22239197 - 26 Nov 2022

Cited by 5 | Viewed by 1799

Abstract

Transportation agencies continuously and consistently work to improve the processes and systems for mitigating the impacts of roadway incidents. Such efforts include utilizing emerging technologies to reduce the detection and response time to roadway incidents. Vehicle-to-infrastructure (V2I) communication is an emerging transportation technology that enables communication between a vehicle and the infrastructure. This paper proposes an algorithm that utilizes V2I probe data to automatically detect roadway incidents. A simulation testbed was developed for a segment of Interstate 64 in St. Louis, Missouri to evaluate the performance of the V2I-based automatic incident detection algorithm. The proposed algorithm was assessed during peak and off-peak periods with various incident durations, under several market penetration rates for V2I technology, and with different spatial resolutions for incident detection. The performance of the proposed algorithm was assessed on the basis of the detection rate, time to detect, detection accuracy, and false alarm rate. The performance measures obtained for the V2I-based automatic incident detection algorithm were compared with California #7 algorithm performance measures. The California #7 algorithm is a traditional automatic incident detection algorithm that utilizes traffic sensors data, such as inductive loop detectors, to identify roadway events. The California #7 algorithm was implemented in the Interstate 64 simulation testbed. The case study results indicated that the proposed V2I-based algorithm outperformed the California #7 algorithm. The detection rate for the proposed V2I-based incident detection algorithm was 100% in market penetrations of 50%, 80%, and 100%. However, the California #7 algorithm’s detection rate was 71%. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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18 pages, 7193 KiB

Open AccessArticle

mm-DSF: A Method for Identifying Dangerous Driving Behaviors Based on the Lateral Fusion of Micro-Doppler Features Combined

by Zhanjun Hao, Zepei Li, Xiaochao Dang, Zhongyu Ma and Yue Wang

Sensors 2022, 22(22), 8929; https://doi.org/10.3390/s22228929 - 18 Nov 2022

Viewed by 1895

Abstract

To address the dangerous driving behaviors prevalent among current car drivers, it is necessary to provide real-time, accurate warning and correction of driver’s driving behaviors in a small, movable, and enclosed space. In this paper, we propose a method for detecting dangerous behaviors based on frequency-modulated continuous-wave radar (mm-DSF). The highly packaged millimeter-wave radar chip has good in-vehicle emotion recognition capability. The acquired millimeter-wave differential frequency signal is Fourier-transformed to obtain the intermediate frequency signal. The physiological decomposition of the local micro-Doppler feature spectrum of the target action is then used as the eigenvalue. Matrix signal intensity and clutter filtering are performed by analyzing the signal echo model of the input channel. The signal classification is based on the estimation and variety of the feature vectors of the target key actions using a modified and optimized level fusion method of the SlowFast dual-channel network. Nine typical risky driving behaviors were set up by the Dula Hazard Questionnaire and TEIQue-SF, and the accuracy of the classification results of the self-built dataset was analyzed to verify the high robustness of the method. The recognition accuracy of this method increased by 1.97% compared with the traditional method. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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13 pages, 5322 KiB

Open AccessArticle

SiamMFC: Visual Object Tracking Based on Mainfold Full Convolution Siamese Network

by Jia Chen, Fan Wang, Yingjie Zhang, Yibo Ai and Weidong Zhang

Sensors 2021, 21(19), 6388; https://doi.org/10.3390/s21196388 - 24 Sep 2021

Viewed by 1889

Abstract

Visual tracking task is divided into classification and regression tasks, and manifold features are introduced to improve the performance of the tracker. Although the previous anchor-based tracker has achieved superior tracking performance, the anchor-based tracker not only needs to set parameters manually but also ignores the influence of the geometric characteristics of the object on the tracker performance. In this paper, we propose a novel Siamese network framework with ResNet50 as the backbone, which is an anchor-free tracker based on manifold features. The network design is simple and easy to understand, which not only considers the influence of geometric features on the target tracking performance but also reduces the calculation of parameters and improves the target tracking performance. In the experiment, we compared our tracker with the most advanced public benchmarks and obtained a state-of-the-art performance. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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14 pages, 6779 KiB

Open AccessArticle

Development of Nationwide Road Quality Map: Remote Sensing Meets Field Sensing

by Sadra Karimzadeh and Masashi Matsuoka

Sensors 2021, 21(6), 2251; https://doi.org/10.3390/s21062251 - 23 Mar 2021

Cited by 6 | Viewed by 3594

Abstract

In this study, we measured the in situ international roughness index (IRI) for first-degree roads spanning more than 1300 km in East Azerbaijan Province, Iran, using a quarter car (QC). Since road quality mapping with in situ measurements is a costly and time-consuming task, we also developed new equations for constructing a road quality proxy map (RQPM) using discriminant analysis and multispectral information from high-resolution Sentinel-2 images, which we calibrated using the in situ data on the basis of geographic information system (GIS) data. The developed equations using optimum index factor (OIF) and norm R provide a valuable tool for creating proxy maps and mitigating hazards at the network scale, not only for primary roads but also for secondary roads, and for reducing the costs of road quality monitoring. The overall accuracy and kappa coefficient of the norm R equation for road classification in East Azerbaijan province are 65.0% and 0.59, respectively. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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30 pages, 6799 KiB

Open AccessArticle

A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

by Vicent Rodrigo Marco, Jens Kalkkuhl, Jörg Raisch and Thomas Seel

Sensors 2021, 21(1), 7; https://doi.org/10.3390/s21010007 - 22 Dec 2020

Cited by 8 | Viewed by 5582

Abstract

Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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27 pages, 5971 KiB

Open AccessArticle

The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

by Yang Li, Yutong Liu, Yanping Wang, Yun Lin and Wenjie Shen

Sensors 2020, 20(18), 5421; https://doi.org/10.3390/s20185421 - 22 Sep 2020

Cited by 30 | Viewed by 8435

Abstract

Compared with the commonly used lidar and visual sensors, the millimeter-wave radar has all-day and all-weather performance advantages and more stable performance in the face of different scenarios. However, using the millimeter-wave radar as the Simultaneous Localization and Mapping (SLAM) sensor is also associated with other problems, such as small data volume, more outliers, and low precision, which reduce the accuracy of SLAM localization and mapping. This paper proposes a millimeter-wave radar SLAM assisted by the Radar Cross Section (RCS) feature of the target and Inertial Measurement Unit (IMU). Using IMU to combine continuous radar scanning point clouds into “Multi-scan,” the problem of small data volume is solved. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is used to filter outliers from radar data. In the clustering, the RCS feature of the target is considered, and the Mahalanobis distance is used to measure the similarity of the radar data. At the same time, in order to alleviate the problem of the lower accuracy of SLAM positioning due to the low precision of millimeter-wave radar data, an improved Correlative Scan Matching (CSM) method is proposed in this paper, which matches the radar point cloud with the local submap of the global grid map. It is a “Scan to Map” point cloud matching method, which achieves the tight coupling of localization and mapping. In this paper, three groups of actual data are collected to verify the proposed method in part and in general. Based on the comparison of the experimental results, it is proved that the proposed millimeter-wave radar SLAM assisted by the RCS feature of the target and IMU has better accuracy and robustness in the face of different scenarios. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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Review

Jump to: Research

18 pages, 1175 KiB

Open AccessReview

A Systematic Review of In-Vehicle Physiological Indices and Sensor Technology for Driver Mental Workload Monitoring

by Ashwini Kanakapura Sriranga, Qian Lu and Stewart Birrell

Sensors 2023, 23(4), 2214; https://doi.org/10.3390/s23042214 - 16 Feb 2023

Cited by 11 | Viewed by 3422

Abstract

The concept of vehicle automation ceases to seem futuristic with the current advancement of the automotive industry. With the introduction of conditional automated vehicles, drivers are no longer expected to focus only on driving activities but are still required to stay alert to resume control. However, fluctuations in driving demands are known to alter the driver’s mental workload (MWL), which might affect the driver’s vehicle take-over capabilities. Driver mental workload can be specified as the driver’s capacity for information processing for task performance. This paper summarizes the literature that relates to analysing driver mental workload through various in-vehicle physiological sensors focusing on cardiovascular and respiratory measures. The review highlights the type of study, hardware, method of analysis, test variable, and results of studies that have used physiological indices for MWL analysis in the automotive context. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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24 pages, 11036 KiB

Open AccessReview

Current Non-Contact Road Surface Condition Detection Schemes and Technical Challenges

by Yao Ma, Meizhu Wang, Qi Feng, Zhiping He and Mi Tian

Sensors 2022, 22(24), 9583; https://doi.org/10.3390/s22249583 - 7 Dec 2022

Cited by 8 | Viewed by 5040

Abstract

Given the continuous improvement in the capabilities of road vehicles to detect obstacles, the road friction coefficient is closely related to vehicular braking control, thus the detection of road surface conditions (RSC), and the level is crucial for driving safety. Non-contact technology for RSC sensing is becoming the main technological and research hotspot for RSC detection because of its fast, non-destructive, efficient, and portable characteristics and attributes. This study started with mapping the relationship between friction coefficients and RSC based on the requirement for autonomous driving. We then compared and analysed the main methods and research application status of non-contact detection schemes. In particular, the use of infrared spectroscopy is expected to be the most approachable technology path to practicality in the field of autonomous driving RSC detection owing to its high accuracy and environmental adaptability properties. We systematically analysed the technical challenges in the practical application of infrared spectroscopy road surface detection, studied the causes, and discussed feasible solutions. Finally, the application prospects and development trends of RSC detection in the fields of automatic driving and exploration robotics are presented and discussed. Full article

(This article belongs to the Special Issue On-Board and Remote Sensors in Intelligent Vehicles)

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