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9587 KiB

Open AccessArticle

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation

by Zutao Zhang, Dianyuan Luo, Yagubov Rasim, Yanjun Li, Guanjun Meng, Jian Xu and Chunbai Wang

Sensors 2016, 16(2), 242; https://doi.org/10.3390/s16020242 - 19 Feb 2016

Cited by 65 | Viewed by 12302

Abstract

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver [...] Read more.

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver’s EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver’s vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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2839 KiB

Open AccessArticle

Intravehicular, Short- and Long-Range Communication Information Fusion for Providing Safe Speed Warnings

by Felipe Jiménez, Jose Eugenio Naranjo, Francisco Serradilla, Elisa Pérez, María Jose Hernández, Trinidad Ruiz, José Javier Anaya and Alberto Díaz

Sensors 2016, 16(1), 131; https://doi.org/10.3390/s16010131 - 21 Jan 2016

Cited by 7 | Viewed by 5880

Abstract

Inappropriate speed is a relevant concurrent factor in many traffic accidents. Moreover, in recent years, traffic accidents numbers in Spain have fallen sharply, but this reduction has not been so significant on single carriageway roads. These infrastructures have less equipment than high-capacity roads, [...] Read more.

Inappropriate speed is a relevant concurrent factor in many traffic accidents. Moreover, in recent years, traffic accidents numbers in Spain have fallen sharply, but this reduction has not been so significant on single carriageway roads. These infrastructures have less equipment than high-capacity roads, therefore measures to reduce accidents on them should be implemented in vehicles. This article describes the development and analysis of the impact on the driver of a warning system for the safe speed on each road section in terms of geometry, the presence of traffic jams, weather conditions, type of vehicle and actual driving conditions. This system is based on an application for smartphones and includes knowledge of the vehicle position via Ground Positioning System (GPS), access to intravehicular information from onboard sensors through the Controller Area Network (CAN) bus, vehicle data entry by the driver, access to roadside information (short-range communications) and access to a centralized server with information about the road in the current and following sections of the route (long-range communications). Using this information, the system calculates the safe speed, recommends the appropriate speed in advance in the following sections and provides warnings to the driver. Finally, data are sent from vehicles to a server to generate new information to disseminate to other users or to supervise drivers’ behaviour. Tests in a driving simulator have been used to define the system warnings and Human Machine Interface (HMI) and final tests have been performed on real roads in order to analyze the effect of the system on driver behavior. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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14170 KiB

Open AccessArticle

Moving Object Detection on a Vehicle Mounted Back-Up Camera

by Dong-Sun Kim and Jinsan Kwon

Sensors 2016, 16(1), 23; https://doi.org/10.3390/s16010023 - 25 Dec 2015

Cited by 23 | Viewed by 8197

Abstract

In the detection of moving objects from vision sources one usually assumes that the scene has been captured by stationary cameras. In case of backing up a vehicle, however, the camera mounted on the vehicle moves according to the vehicle’s movement, resulting in [...] Read more.

In the detection of moving objects from vision sources one usually assumes that the scene has been captured by stationary cameras. In case of backing up a vehicle, however, the camera mounted on the vehicle moves according to the vehicle’s movement, resulting in ego-motions on the background. This results in mixed motion in the scene, and makes it difficult to distinguish between the target objects and background motions. Without further treatments on the mixed motion, traditional fixed-viewpoint object detection methods will lead to many false-positive detection results. In this paper, we suggest a procedure to be used with the traditional moving object detection methods relaxing the stationary cameras restriction, by introducing additional steps before and after the detection. We also decribe the implementation as a FPGA platform along with the algorithm. The target application of this suggestion is use with a road vehicle’s rear-view camera systems. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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8698 KiB

Open AccessArticle

Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching

by Guohua Wang and Qiong Liu

Sensors 2015, 15(12), 32188-32212; https://doi.org/10.3390/s151229874 - 21 Dec 2015

Cited by 19 | Viewed by 7987

Abstract

Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a [...] Read more.

Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians’ head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians’ size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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3356 KiB

Open AccessArticle

Robust Road Condition Detection System Using In-Vehicle Standard Sensors

by Juan Jesús Castillo Aguilar, Juan Antonio Cabrera Carrillo, Antonio Jesús Guerra Fernández and Enrique Carabias Acosta

Sensors 2015, 15(12), 32056-32078; https://doi.org/10.3390/s151229908 - 19 Dec 2015

Cited by 30 | Viewed by 13859

Abstract

The appearance of active safety systems, such as Anti-lock Braking System, Traction Control System, Stability Control System, etc., represents a major evolution in road safety. In the automotive sector, the term vehicle active safety systems refers to those whose goal is to [...] Read more.

The appearance of active safety systems, such as Anti-lock Braking System, Traction Control System, Stability Control System, etc., represents a major evolution in road safety. In the automotive sector, the term vehicle active safety systems refers to those whose goal is to help avoid a crash or to reduce the risk of having an accident. These systems safeguard us, being in continuous evolution and incorporating new capabilities continuously. In order for these systems and vehicles to work adequately, they need to know some fundamental information: the road condition on which the vehicle is circulating. This early road detection is intended to allow vehicle control systems to act faster and more suitably, thus obtaining a substantial advantage. In this work, we try to detect the road condition the vehicle is being driven on, using the standard sensors installed in commercial vehicles. Vehicle models were programmed in on-board systems to perform real-time estimations of the forces of contact between the wheel and road and the speed of the vehicle. Subsequently, a fuzzy logic block is used to obtain an index representing the road condition. Finally, an artificial neural network was used to provide the optimal slip for each surface. Simulations and experiments verified the proposed method. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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2741 KiB

Open AccessArticle

An Accurate and Generic Testing Approach to Vehicle Stability Parameters Based on GPS and INS

by Zhibin Miao, Hongtian Zhang and Jinzhu Zhang

Sensors 2015, 15(12), 30469-30486; https://doi.org/10.3390/s151229812 - 04 Dec 2015

Cited by 6 | Viewed by 7517

Abstract

With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. As a common method, usually GPS sensors and INS sensors are applied to measure vehicle stability parameters by fusing [...] Read more.

With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. As a common method, usually GPS sensors and INS sensors are applied to measure vehicle stability parameters by fusing data from the two system sensors. Although prior model parameters should be recognized in a Kalman filter, it is usually used to fuse data from multi-sensors. In this paper, a robust, intelligent and precise method to the measurement of vehicle stability is proposed. First, a fuzzy interpolation method is proposed, along with a four-wheel vehicle dynamic model. Second, a two-stage Kalman filter, which fuses the data from GPS and INS, is established. Next, this approach is applied to a case study vehicle to measure yaw rate and sideslip angle. The results show the advantages of the approach. Finally, a simulation and real experiment is made to verify the advantages of this approach. The experimental results showed the merits of this method for measuring vehicle stability, and the approach can meet the design requirements of a vehicle stability controller. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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10002 KiB

Open AccessArticle

Passive Sensor Integration for Vehicle Self-Localization in Urban Traffic Environment

by Yanlei Gu, Li-Ta Hsu and Shunsuke Kamijo

Sensors 2015, 15(12), 30199-30220; https://doi.org/10.3390/s151229795 - 03 Dec 2015

Cited by 47 | Viewed by 9275

Abstract

This research proposes an accurate vehicular positioning system which can achieve lane-level performance in urban canyons. Multiple passive sensors, which include Global Navigation Satellite System (GNSS) receivers, onboard cameras and inertial sensors, are integrated in the proposed system. As the main source for [...] Read more.

This research proposes an accurate vehicular positioning system which can achieve lane-level performance in urban canyons. Multiple passive sensors, which include Global Navigation Satellite System (GNSS) receivers, onboard cameras and inertial sensors, are integrated in the proposed system. As the main source for the localization, the GNSS technique suffers from Non-Line-Of-Sight (NLOS) propagation and multipath effects in urban canyons. This paper proposes to employ a novel GNSS positioning technique in the integration. The employed GNSS technique reduces the multipath and NLOS effects by using the 3D building map. In addition, the inertial sensor can describe the vehicle motion, but has a drift problem as time increases. This paper develops vision-based lane detection, which is firstly used for controlling the drift of the inertial sensor. Moreover, the lane keeping and changing behaviors are extracted from the lane detection function, and further reduce the lateral positioning error in the proposed localization system. We evaluate the integrated localization system in the challenging city urban scenario. The experiments demonstrate the proposed method has sub-meter accuracy with respect to mean positioning error. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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2736 KiB

Open AccessArticle

Pothole Detection System Using a Black-box Camera

by Youngtae Jo and Seungki Ryu

Sensors 2015, 15(11), 29316-29331; https://doi.org/10.3390/s151129316 - 19 Nov 2015

Cited by 85 | Viewed by 27853

Abstract

Aging roads and poor road-maintenance systems result a large number of potholes, whose numbers increase over time. Potholes jeopardize road safety and transportation efficiency. Moreover, they are often a contributing factor to car accidents. To address the problems associated with potholes, the locations [...] Read more.

Aging roads and poor road-maintenance systems result a large number of potholes, whose numbers increase over time. Potholes jeopardize road safety and transportation efficiency. Moreover, they are often a contributing factor to car accidents. To address the problems associated with potholes, the locations and size of potholes must be determined quickly. Sophisticated road-maintenance strategies can be developed using a pothole database, which requires a specific pothole-detection system that can collect pothole information at low cost and over a wide area. However, pothole repair has long relied on manual detection efforts. Recent automatic detection systems, such as those based on vibrations or laser scanning, are insufficient to detect potholes correctly and inexpensively owing to the unstable detection of vibration-based methods and high costs of laser scanning-based methods. Thus, in this paper, we introduce a new pothole-detection system using a commercial black-box camera. The proposed system detects potholes over a wide area and at low cost. We have developed a novel pothole-detection algorithm specifically designed to work with the embedded computing environments of black-box cameras. Experimental results are presented with our proposed system, showing that potholes can be detected accurately in real-time. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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2334 KiB

Open AccessArticle

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

by Monica Villaverde, David Perez and Felix Moreno

Sensors 2015, 15(11), 29056-29078; https://doi.org/10.3390/s151129056 - 17 Nov 2015

Cited by 4 | Viewed by 5927

Abstract

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this [...] Read more.

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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5098 KiB

Open AccessArticle

Application of Novel Lateral Tire Force Sensors to Vehicle Parameter Estimation of Electric Vehicles

by Kanghyun Nam

Sensors 2015, 15(11), 28385-28401; https://doi.org/10.3390/s151128385 - 11 Nov 2015

Cited by 25 | Viewed by 8825

Abstract

This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented [...] Read more.

This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented and further developed by NSK Ltd. For estimating the lateral vehicle velocity, tire force models considering lateral load transfer effects are used, and a recursive least square algorithm is adapted to identify the lateral vehicle velocity as an unknown parameter. Using the estimated lateral vehicle velocity, tire cornering stiffness, which is an important tire parameter dominating the vehicle’s cornering responses, is estimated. For the practical implementation, the cornering stiffness estimation algorithm based on a simple bicycle model is developed and discussed. Finally, proposed estimation algorithms were evaluated using experimental test data. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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2733 KiB

Open AccessArticle

Vehicle Classification Using the Discrete Fourier Transform with Traffic Inductive Sensors

by José J. Lamas-Seco, Paula M. Castro, Adriana Dapena and Francisco J. Vazquez-Araujo

Sensors 2015, 15(10), 27201-27214; https://doi.org/10.3390/s151027201 - 26 Oct 2015

Cited by 30 | Viewed by 6247

Abstract

Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to [...] Read more.

Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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1638 KiB

Open AccessArticle

An LPV Adaptive Observer for Updating a Map Applied to an MAF Sensor in a Diesel Engine

by Zhiyuan Liu and Changhui Wang

Sensors 2015, 15(10), 27142-27159; https://doi.org/10.3390/s151027142 - 23 Oct 2015

Cited by 10 | Viewed by 6564

Abstract

In this paper, a new method for mass air flow (MAF) sensor error compensation and an online updating error map (or lookup table) due to installation and aging in a diesel engine is developed. Since the MAF sensor error is dependent on the [...] Read more.

In this paper, a new method for mass air flow (MAF) sensor error compensation and an online updating error map (or lookup table) due to installation and aging in a diesel engine is developed. Since the MAF sensor error is dependent on the engine operating point, the error model is represented as a two-dimensional (2D) map with two inputs, fuel mass injection quantity and engine speed. Meanwhile, the 2D map representing the MAF sensor error is described as a piecewise bilinear interpolation model, which can be written as a dot product between the regression vector and parameter vector using a membership function. With the combination of the 2D map regression model and the diesel engine air path system, an LPV adaptive observer with low computational load is designed to estimate states and parameters jointly. The convergence of the proposed algorithm is proven under the conditions of persistent excitation and given inequalities. The observer is validated against the simulation data from engine software enDYNA provided by Tesis. The results demonstrate that the operating point-dependent error of the MAF sensor can be approximated acceptably by the 2D map from the proposed method. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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3025 KiB

Open AccessArticle

Data Fusion for Driver Behaviour Analysis

by Juan Carmona, Fernando García, David Martín, Arturo De la Escalera and José María Armingol

Sensors 2015, 15(10), 25968-25991; https://doi.org/10.3390/s151025968 - 14 Oct 2015

Cited by 56 | Viewed by 9319

Abstract

A driver behaviour analysis tool is presented. The proposal offers a novel contribution based on low-cost hardware and advanced software capabilities based on data fusion. The device takes advantage of the information provided by the in-vehicle sensors using Controller Area Network Bus (CAN-BUS), [...] Read more.

A driver behaviour analysis tool is presented. The proposal offers a novel contribution based on low-cost hardware and advanced software capabilities based on data fusion. The device takes advantage of the information provided by the in-vehicle sensors using Controller Area Network Bus (CAN-BUS), an Inertial Measurement Unit (IMU) and a GPS. By fusing this information, the system can infer the behaviour of the driver, providing aggressive behaviour detection. By means of accurate GPS-based localization, the system is able to add context information, such as digital map information, speed limits, etc. Several parameters and signals are taken into account, both in the temporal and frequency domains, to provide real time behaviour detection. The system was tested in urban, interurban and highways scenarios. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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8792 KiB

Open AccessArticle

A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment

by Jian Liu, Huawei Liang, Zhiling Wang and Xiangcheng Chen

Sensors 2015, 15(9), 21931-21956; https://doi.org/10.3390/s150921931 - 31 Aug 2015

Cited by 15 | Viewed by 6196

Abstract

The quick and accurate understanding of the ambient environment, which is composed of road curbs, vehicles, pedestrians, etc., is critical for developing intelligent vehicles. The road elements included in this work are road curbs and dynamic road obstacles that directly affect the [...] Read more.

The quick and accurate understanding of the ambient environment, which is composed of road curbs, vehicles, pedestrians, etc., is critical for developing intelligent vehicles. The road elements included in this work are road curbs and dynamic road obstacles that directly affect the drivable area. A framework for the online modeling of the driving environment using a multi-beam LIDAR, i.e., a Velodyne HDL-64E LIDAR, which describes the 3D environment in the form of a point cloud, is reported in this article. First, ground segmentation is performed via multi-feature extraction of the raw data grabbed by the Velodyne LIDAR to satisfy the requirement of online environment modeling. Curbs and dynamic road obstacles are detected and tracked in different manners. Curves are fitted for curb points, and points are clustered into bundles whose form and kinematics parameters are calculated. The Kalman filter is used to track dynamic obstacles, whereas the snake model is employed for curbs. Results indicate that the proposed framework is robust under various environments and satisfies the requirements for online processing. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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1356 KiB

Open AccessArticle

GPS/DR Error Estimation for Autonomous Vehicle Localization

by Byung-Hyun Lee, Jong-Hwa Song, Jun-Hyuck Im, Sung-Hyuck Im, Moon-Beom Heo and Gyu-In Jee

Sensors 2015, 15(8), 20779-20798; https://doi.org/10.3390/s150820779 - 21 Aug 2015

Cited by 69 | Viewed by 10596

Abstract

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections [...] Read more.

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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1992 KiB

Open AccessArticle

Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

by Wanzeng Kong, Weicheng Lin, Fabio Babiloni, Sanqing Hu and Gianluca Borghini

Sensors 2015, 15(8), 19181-19198; https://doi.org/10.3390/s150819181 - 05 Aug 2015

Cited by 70 | Viewed by 7498

Abstract

Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level [...] Read more.

Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG) signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain’s ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC) in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers’ fatigue levels, and as reference work for future studies. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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2695 KiB

Open AccessArticle

Improving Localization Accuracy: Successive Measurements Error Modeling

by Najah Abu Ali and Mervat Abu-Elkheir

Sensors 2015, 15(7), 15540-15561; https://doi.org/10.3390/s150715540 - 01 Jul 2015

Cited by 9 | Viewed by 6047

Abstract

Vehicle self-localization is an essential requirement for many of the safety applications envisioned for vehicular networks. The mathematical models used in current vehicular localization schemes focus on modeling the localization error itself, and overlook the potential correlation between successive localization measurement errors. In [...] Read more.

Vehicle self-localization is an essential requirement for many of the safety applications envisioned for vehicular networks. The mathematical models used in current vehicular localization schemes focus on modeling the localization error itself, and overlook the potential correlation between successive localization measurement errors. In this paper, we first investigate the existence of correlation between successive positioning measurements, and then incorporate this correlation into the modeling positioning error. We use the Yule Walker equations to determine the degree of correlation between a vehicle’s future position and its past positions, and then propose a -order Gauss–Markov model to predict the future position of a vehicle from its past positions. We investigate the existence of correlation for two datasets representing the mobility traces of two vehicles over a period of time. We prove the existence of correlation between successive measurements in the two datasets, and show that the time correlation between measurements can have a value up to four minutes. Through simulations, we validate the robustness of our model and show that it is possible to use the first-order Gauss–Markov model, which has the least complexity, and still maintain an accurate estimation of a vehicle’s future location over time using only its current position. Our model can assist in providing better modeling of positioning errors and can be used as a prediction tool to improve the performance of classical localization algorithms such as the Kalman filter. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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Open AccessArticle

Radar Sensing for Intelligent Vehicles in Urban Environments

by Giulio Reina, David Johnson and James Underwood

Sensors 2015, 15(6), 14661-14678; https://doi.org/10.3390/s150614661 - 19 Jun 2015

Cited by 68 | Viewed by 9684

Abstract

Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios. This paper presents a novel method for ground and obstacle segmentation based on radar sensing. The algorithm operates directly in the [...] Read more.

Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios. This paper presents a novel method for ground and obstacle segmentation based on radar sensing. The algorithm operates directly in the sensor frame, without the need for a separate synchronised navigation source, calibration parameters describing the location of the radar in the vehicle frame, or the geometric restrictions made in the previous main method in the field. Experimental results are presented in various urban scenarios to validate this approach, showing its potential applicability for advanced driving assistance systems and autonomous vehicle operations. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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Open AccessArticle

Human-Automation Interaction Design for Adaptive Cruise Control Systems of Ground Vehicles

by Hwisoo Eom and Sang Hun Lee

Sensors 2015, 15(6), 13916-13944; https://doi.org/10.3390/s150613916 - 12 Jun 2015

Cited by 20 | Viewed by 11891

Abstract

A majority of recently developed advanced vehicles have been equipped with various automated driver assistance systems, such as adaptive cruise control (ACC) and lane keeping assistance systems. ACC systems have several operational modes, and drivers can be unaware of the mode in which [...] Read more.

A majority of recently developed advanced vehicles have been equipped with various automated driver assistance systems, such as adaptive cruise control (ACC) and lane keeping assistance systems. ACC systems have several operational modes, and drivers can be unaware of the mode in which they are operating. Because mode confusion is a significant human error factor that contributes to traffic accidents, it is necessary to develop user interfaces for ACC systems that can reduce mode confusion. To meet this requirement, this paper presents a new human-automation interaction design methodology in which the compatibility of the machine and interface models is determined using the proposed criteria, and if the models are incompatible, one or both of the models is/are modified to make them compatible. To investigate the effectiveness of our methodology, we designed two new interfaces by separately modifying the machine model and the interface model and then performed driver-in-the-loop experiments. The results showed that modifying the machine model provides a more compact, acceptable, effective, and safe interface than modifying the interface model. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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3392 KiB

Open AccessArticle

Provisioning Vehicular Services and Communications Based on a Bluetooth Sensor Network Deployment

by David Perez-Diaz De Cerio and José Luis Valenzuela

Sensors 2015, 15(6), 12765-12781; https://doi.org/10.3390/s150612765 - 29 May 2015

Cited by 9 | Viewed by 7081

Abstract

It is very common to rule out Bluetooth as a suitable technology for vehicular communications. The reasons behind this decision usually result from misconceptions such as accepting that Bluetooth has a short application range, or assuming its connection setup is not fast enough [...] Read more.

It is very common to rule out Bluetooth as a suitable technology for vehicular communications. The reasons behind this decision usually result from misconceptions such as accepting that Bluetooth has a short application range, or assuming its connection setup is not fast enough to allow communication which involves high speed moving nodes. This paper refutes those assertions and proposes the use of Bluetooth not only for Infrastructure-to-Vehicle (I2V) or Road-to-Vehicle (R2V) communications, but also for Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communications. This novel proposal is based on using the remote name request procedure of the standard, combined with an adjustment and optimization of the parameters present in the inquiry and page procedures. The proposed modifications reduce the information exchange delay, thus making Bluetooth a suitable technology for high-speed vehicle communications. The feasibility of the proposed scheme has been validated through experimental tests conducted in different scenarios: laboratory, a real highway and a racing test circuit. There, the communication system was installed in a vehicle circulating at speeds of up to 250 km/h, whereas autonomous devices were disseminated throughout the road path to communicate with the on board devices obtaining satisfying results. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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3060 KiB

Open AccessArticle

Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set

by Liangjiang Yu, Guoliang Fan, Jiulu Gong and Joseph P. Havlicek

Sensors 2015, 15(5), 10118-10145; https://doi.org/10.3390/s150510118 - 29 Apr 2015

Cited by 12 | Viewed by 8891

Abstract

We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where [...] Read more.

We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation). Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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5334 KiB

Open AccessArticle

Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes

by J. Javier Yebes, Luis M. Bergasa and Miguel García-Garrido

Sensors 2015, 15(4), 9228-9250; https://doi.org/10.3390/s150409228 - 20 Apr 2015

Cited by 25 | Viewed by 9002

Abstract

Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing [...] Read more.

Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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1025 KiB

Open AccessArticle

An Ultrasonic Sensor System Based on a Two-Dimensional State Method for Highway Vehicle Violation Detection Applications

by Jun Liu, Jiuqiang Han, Hongqiang Lv and Bing Li

Sensors 2015, 15(4), 9000-9021; https://doi.org/10.3390/s150409000 - 16 Apr 2015

Cited by 19 | Viewed by 9567

Abstract

With the continuing growth of highway construction and vehicle use expansion all over the world, highway vehicle traffic rule violation (TRV) detection has become more and more important so as to avoid traffic accidents and injuries in intelligent transportation systems (ITS) and vehicular [...] Read more.

With the continuing growth of highway construction and vehicle use expansion all over the world, highway vehicle traffic rule violation (TRV) detection has become more and more important so as to avoid traffic accidents and injuries in intelligent transportation systems (ITS) and vehicular ad hoc networks (VANETs). Since very few works have contributed to solve the TRV detection problem by moving vehicle measurements and surveillance devices, this paper develops a novel parallel ultrasonic sensor system that can be used to identify the TRV behavior of a host vehicle in real-time. Then a two-dimensional state method is proposed, utilizing the spacial state and time sequential states from the data of two parallel ultrasonic sensors to detect and count the highway vehicle violations. Finally, the theoretical TRV identification probability is analyzed, and actual experiments are conducted on different highway segments with various driving speeds, which indicates that the identification accuracy of the proposed method can reach about 90.97%. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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3204 KiB

Open AccessArticle

Pedestrian Detection in Far-Infrared Daytime Images Using a Hierarchical Codebook of SURF

by Bassem Besbes, Alexandrina Rogozan, Adela-Maria Rus, Abdelaziz Bensrhair and Alberto Broggi

Sensors 2015, 15(4), 8570-8594; https://doi.org/10.3390/s150408570 - 13 Apr 2015

Cited by 31 | Viewed by 8028

Abstract

One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this [...] Read more.

One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this paper, we propose a pedestrian detector with on-board FIR camera. Our main contribution is the exploitation of the specific characteristics of FIR images to design a fast, scale-invariant and robust pedestrian detector. Our system consists of three modules, each based on speeded-up robust feature (SURF) matching. The first module allows generating regions-of-interest (ROI), since in FIR images of the pedestrian shapes may vary in large scales, but heads appear usually as light regions. ROI are detected with a high recall rate with the hierarchical codebook of SURF features located in head regions. The second module consists of pedestrian full-body classification by using SVM. This module allows one to enhance the precision with low computational cost. In the third module, we combine the mean shift algorithm with inter-frame scale-invariant SURF feature tracking to enhance the robustness of our system. The experimental evaluation shows that our system outperforms, in the FIR domain, the state-of-the-art Haar-like Adaboost-cascade, histogram of oriented gradients (HOG)/linear SVM (linSVM) and MultiFtrpedestrian detectors, trained on the FIR images. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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940 KiB

Open AccessArticle

Sensor4PRI: A Sensor Platform for the Protection of Railway Infrastructures

by Eduardo Cañete, Jaime Chen, Manuel Díaz, Luis Llopis and Bartolomé Rubio

Sensors 2015, 15(3), 4996-5019; https://doi.org/10.3390/s150304996 - 27 Feb 2015

Cited by 16 | Viewed by 9373

Abstract

Wireless Sensor Networks constitute pervasive and distributed computing systems and are potentially one of the most important technologies of this century. They have been specifically identified as a good candidate to become an integral part of the protection of critical infrastructures. In this [...] Read more.

Wireless Sensor Networks constitute pervasive and distributed computing systems and are potentially one of the most important technologies of this century. They have been specifically identified as a good candidate to become an integral part of the protection of critical infrastructures. In this paper we focus on railway infrastructure protection and we present the details of a sensor platform designed to be integrated into a slab track system in order to carry out both installation and maintenance monitoring activities. In the installation phase, the platform helps operators to install the slab tracks in the right position. In the maintenance phase, the platform collects information about the structural health and behavior of the infrastructure when a train travels along it and relays the readings to a base station. The base station uses trains as data mules to upload the information to the internet. The use of a train as a data mule is especially suitable for collecting information from remote or inaccessible places which do not have a direct connection to the internet and require less network infrastructure. The overall aim of the system is to deploy a permanent economically viable monitoring system to improve the safety of railway infrastructures. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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Review

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4901 KiB

Open AccessReview

Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety

by Angelica Reyes-Muñoz, Mari Carmen Domingo, Marco Antonio López-Trinidad and José Luis Delgado

Sensors 2016, 16(1), 107; https://doi.org/10.3390/s16010107 - 15 Jan 2016

Cited by 38 | Viewed by 11594

Abstract

The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must [...] Read more.

The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels. Full article

(This article belongs to the Special Issue Sensors in New Road Vehicles)

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