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Road Vehicles Surroundings Supervision: On-Board Sensors and Communications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (10 February 2018) | Viewed by 68910

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Special Issue Editor

Dr. Felipe Jiménez

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Guest Editor

Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: intelligent transport systems; advanced driver assistance systems; vehicle positioning; inertial sensors; digital maps; vehicle dynamics; driver monitoring; perception; autonomous vehicles; cooperative services; connected and autonomous driving
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Special Issue Information

Dear Colleagues,

New assistance systems, cooperative and autonomous driving of road vehicles, imply an accurate knowledge of vehicle surroundings. This knowledge can come from different sources, such as on-board sensors, sensors in the infrastructure, and communications.

Among onboard sensors, short- and long-range sensors can be distinguished. In the first case, ultrasonic, infrared, and capacitive sensors can be cited. Among the second group, laser scanners and computer vision technologies appear to provide the best performance, although there are many others that can complement the information using data fusion processes. In any case, the goal is to have a representation of vehicle surroundings that is as complete as possible. In any case, sensor fusion algorithms are a common solution to overcome the single sensors limitations.

Vehicle positioning is also essential. For this purpose, satellite positioning is commonly used, but when it is not sufficiently reliable or the signal is lost, the same technologies as those used for the recognition of the surroundings can provide an acceptable solution. In this regard, it should be noted that the SLAM (Simultaneous Localization and Mapping) problem, tries to build or update the map of an environment that is not known a priori, and positioning the vehicle on that map simultaneously. This technique, widely used and proven in robotics, has also been implemented in the vehicular field for the perception of the environment in real time, support, and accuracy improvements in autonomous GNSS navigation, and generation of digital maps or calculation of trajectories followed when no GPS signal is received.

Vehicle-to-vehicle (V2V) communications and vehicle-to-infrastructure (V2I) communications allow the vehicle to have greater knowledge of surroundings and to obtain information that is far away from the onboard sensors. These communications provide additional data that could be used for driver information purposes or for decision modules in autonomous driving, for example. In this sense, connected and cooperative driving appears as a catalyst of autonomous driving, because it enables real deployment under complex driving situations.

Apart from original research related to the topic, studies on the state-of-the-art in relation to previous works are also welcome.

In conclusion, the aim of this Special Issue is to bring together innovative developments related to the use of technologies in road vehicles to obtain information about vehicle surroundings, mainly for vehicle surroundings supervision and reconstruction, including, but not limited to:

Laser scanner for automotive uses
Computer vision for automotive uses
Vehicle surroundings supervision
Vehicle surroundings reconstruction
Obstacles detection
Trajectory estimation
Data fusion
Visual SLAM
Driver assistance systems
Autonomous driving
V2X communications
Connected vehicles
Cooperative driving

Authors are invited to contact the Guest Editor, prior to submission, if they are uncertain whether their work falls within the general scope of this Special Issue.

Dr. Felipe Jimenez
Guest Editor

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Keywords

Laser scanner
Computer vision
Vehicle surroundings supervision
Vehicle surroundings reconstruction
Obstacles detection
Trajectory estimation
Data fusion
Visual SLAM
Driver assistance systems
Autonomous driving
V2X communications
Connected vehicles
Cooperative driving

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Published Papers (12 papers)

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Editorial

Jump to: Research

3 pages, 190 KiB

Open AccessEditorial

Road Vehicles Surroundings Supervision: Onboard Sensors and Communications

by Felipe Jiménez

Appl. Sci. 2018, 8(7), 1125; https://doi.org/10.3390/app8071125 - 11 Jul 2018

Cited by 1 | Viewed by 2480

Abstract

This Special Issue covers some of the most relevant topics related to road vehicle surroundings supervision, providing an overview of technologies and algorithms that are currently under research and deployment. This supervision is essential for the new applications in current vehicles oriented to connected and autonomous driving. The first part deals with specific technologies or solutions, including onboard sensors, communications, driver supervision, and traffic analysis, and the second one presents applications or architectures for autonomous vehicles (or parts of them). Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

Research

Jump to: Editorial

13 pages, 2019 KiB

Open AccessArticle

Accurate and Detailed Transversal Road Section Characteristics Extraction Using Laser Scanner

by Felipe Jiménez, Miguel Clavijo, Fernando Castellanos and Carlos Álvarez

Appl. Sci. 2018, 8(5), 724; https://doi.org/10.3390/app8050724 - 5 May 2018

Cited by 11 | Viewed by 4632

Abstract

Road vehicle lateral positioning is a key aspect of many assistance applications and autonomous driving. However, conventional GNSS-based positioning systems and fusion with inertial systems are not able to achieve these levels of accuracy under real traffic conditions. Onboard perception systems provide knowledge of the surroundings of the vehicle, and some algorithms have been proposed to detect road boundaries and lane lines. This information could be used to locate the vehicle in the lane. However, most proposed algorithms are quite partial and do not take advantage of a complete knowledge of the road section. This paper proposes an integrated approach to the two tasks that provides a higher level of robustness of results: road boundaries detection and lane lines detection. Furthermore, the algorithm is not restricted to certain scenarios such as the detection of curbs; it could be also used in off-road tracks. The functions have been tested in real environments and their capabilities for autonomous driving have been verified. The algorithm is ready to be merged with digital map information; this development would improve results accuracy. Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

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18 pages, 1576 KiB

Open AccessArticle

Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment

by Irshad Ahmed Abbasi, Adnan Shahid Khan and Shahzad Ali

Appl. Sci. 2018, 8(5), 687; https://doi.org/10.3390/app8050687 - 28 Apr 2018

Cited by 26 | Viewed by 5527

Abstract

VANET (Vehicular Ad-hoc Network) is an emerging offshoot of MANETs (Mobile Ad-hoc Networks) with highly mobile nodes. It is envisioned to play a vital role in providing safety communications and commercial applications to the on-road public. Establishing an optimal route for vehicles to send packets to their respective destinations in VANETs is challenging because of quick speed of vehicles, dynamic nature of the network, and intermittent connectivity among nodes. This paper presents a novel position based routing technique called Dynamic Multiple Junction Selection based Routing (DMJSR) for the city environment. The novelty of DMJSR as compared to existing approaches comes from its novel dynamic multiple junction selection mechanism and an improved greedy forwarding mechanism based on one-hop neighbors between the junctions. To the best of our knowledge, it is the first ever attempt to study the impact of multiple junction selection mechanism on routing in VANETs. We present a detailed depiction of our protocol and the improvements it brings as compared to existing routing strategies. The simulation study exhibits that our proposed protocol outperforms the existing protocols like Geographic Source Routing Protocol (GSR), Enhanced Greedy Traffic Aware Routing Protocol (E-GyTAR) and Traffic Flow Oriented Routing Protocol (TFOR) in terms of packet delivery ratio, end-to-end delay, and routing overhead. Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

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9 pages, 867 KiB

Open AccessArticle

A Multiple-Model Particle Filter Fusion Algorithm for GNSS/DR Slide Error Detection and Compensation

by Rafael Toledo-Moreo, Carlos Colodro-Conde and Javier Toledo-Moreo

Appl. Sci. 2018, 8(3), 445; https://doi.org/10.3390/app8030445 - 15 Mar 2018

Cited by 6 | Viewed by 4070

Abstract

Continuous accurate positioning is a key element for the deployment of many advanced driver assistance systems (ADAS) and autonomous vehicle navigation. To achieve the necessary performance, global navigation satellite systems (GNSS) must be combined with other technologies. A common onboard sensor-set that allows keeping the cost low, features the GNSS unit, odometry, and inertial sensors, such as a gyro. Odometry and inertial sensors compensate for GNSS flaws in many situations and, in normal conditions, their errors can be easily characterized, thus making the whole solution not only more accurate but also with more integrity. However, odometers do not behave properly when friction conditions make the tires slide. If not properly considered, the positioning perception will not be sound. This article introduces a hybridization approach that takes into consideration the sliding situations by means of a multiple model particle filter (MMPF). Tests with real datasets show the goodness of the proposal. Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

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20 pages, 7116 KiB

Open AccessArticle

Cloud Incubator Car: A Reliable Platform for Autonomous Driving

by Raúl Borraz, Pedro J. Navarro, Carlos Fernández and Pedro María Alcover

Appl. Sci. 2018, 8(2), 303; https://doi.org/10.3390/app8020303 - 20 Feb 2018

Cited by 18 | Viewed by 6887

Abstract

It appears clear that the future of road transport is going through enormous changes (intelligent transport systems), the main one being the Intelligent Vehicle (IV). Automated driving requires a huge research effort in multiple technological areas: sensing, control, and driving algorithms. We present a comprehensible and reliable platform for autonomous driving technology development as well as for testing purposes, developed in the Intelligent Vehicles Lab at the Technical University of Cartagena. We propose an open and modular architecture capable of easily integrating a wide variety of sensors and actuators which can be used for testing algorithms and control strategies. As a proof of concept, this paper presents a reliable and complete navigation application for a commercial vehicle (Renault Twizy). It comprises a complete perception system (2D LIDAR, 3D HD LIDAR, ToF cameras, Real-Time Kinematic (RTK) unit, Inertial Measurement Unit (IMU)), an automation of the driving elements of the vehicle (throttle, steering, brakes, and gearbox), a control system, and a decision-making system. Furthermore, two flexible and reliable algorithms are presented for carrying out global and local route planning on board autonomous vehicles. Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

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20 pages, 1499 KiB

Open AccessArticle

Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks

by Jianbin Chen, Demin Li, Guanglin Zhang and Xiaolu Zhang

Appl. Sci. 2018, 8(2), 277; https://doi.org/10.3390/app8020277 - 12 Feb 2018

Cited by 15 | Viewed by 4190

Abstract

With the rapid increase of private vehicles, traffic congestion has become a worldwide problem. Various models have been proposed to undertake traffic prediction. Among them, autoregressive integrated moving average (ARIMA) models are quite popular for their good performance (simple, low complexity, etc.) in traffic prediction. Localized Space-Time ARIMA (LSTARIMA) improves ARIMA’s prediction accuracy by extending the widely used STARIMA with a dynamic weight matrix. In this paper, a localized space-time autoregressive (LSTAR) model was proposed and a new parameters estimation method was formulated based on the LSTARIMA model to reduce computational complexity for real-time prediction purposes. Moreover, two theorems are given and verified for parameter estimation of our proposed LSTAR model. The simulation results showed that LSTAR provided better prediction accuracy when compared to other time series models such as Shift, autoregressive (AR), seasonal moving average (Seasonal MA), and Space-Time AR (STAR). We found that the prediction accuracy of LSTAR was a bit lower than the LSTARIMA model in the simulation results. However, the computational complexity of the LSTAR model was also lower than the LSTARIMA model. Therefore, there exists a tradeoff between the prediction accuracy and the computational complexity for the two models. Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

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2581 KiB

Open AccessArticle

Study on Driving Decision-Making Mechanism of Autonomous Vehicle Based on an Optimized Support Vector Machine Regression

by Junyou Zhang, Yaping Liao, Shufeng Wang and Jian Han

Appl. Sci. 2018, 8(1), 13; https://doi.org/10.3390/app8010013 - 22 Dec 2017

Cited by 43 | Viewed by 8387

Abstract

Driving Decision-making Mechanism (DDM) is identified as the key technology to ensure the driving safety of autonomous vehicle, which is mainly influenced by vehicle states and road conditions. However, previous studies have seldom considered road conditions and their coupled effects on driving decisions. Therefore, road conditions are introduced into DDM in this paper, and are based on a Support Vector Machine Regression (SVR) model, which is optimized by a weighted hybrid kernel function and a Particle Swarm Optimization (PSO) algorithm, this study designs a DDM for autonomous vehicle. Then, the SVR model with RBF (Radial Basis Function) kernel function and BP (Back Propagation) neural network model are tested to validate the accuracy of the optimized SVR model. The results show that the optimized SVR model has the best performance than other two models. Finally, the effects of road conditions on driving decisions are analyzed quantitatively by comparing the reasoning results of DDM with different reference index combinations, and by the sensitivity analysis of DDM with added road conditions. The results demonstrate the significant improvement in the performance of DDM with added road conditions. It also shows that road conditions have the greatest influence on driving decisions at low traffic density, among those, the most influential is road visibility, then followed by adhesion coefficient, road curvature and road slope, while at high traffic density, they have almost no influence on driving decisions. Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

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6088 KiB

Open AccessArticle

An Experimental Platform for Autonomous Bus Development

by Héctor Montes, Carlota Salinas, Roemi Fernández and Manuel Armada

Appl. Sci. 2017, 7(11), 1131; https://doi.org/10.3390/app7111131 - 2 Nov 2017

Cited by 24 | Viewed by 9645

Abstract

Nowadays, with highly developed instrumentation, sensing and actuation technologies, it is possible to foresee an important advance in the field of autonomous and/or semi-autonomous transportation systems. Intelligent Transport Systems (ITS) have been subjected to very active research for many years, and Bus Rapid Transit (BRT) is one area of major interest. Among the most promising transport infrastructures, the articulated bus is an interesting, low cost, high occupancy capacity and friendly option. In this paper, an experimental platform for research on the automatic control of an articulated bus is presented. The aim of the platform is to allow full experimentation in real conditions for testing technological developments and control algorithms. The experimental platform consists of a mobile component (a commercial articulated bus) fully instrumented and a ground test area composed of asphalt roads inside the Consejo Superior de Investigaciones Científicas (CSIC) premises. This paper focuses also on the development of a human machine interface to ease progress in control system evaluation. Some experimental results are presented in order to show the potential of the proposed platform. Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

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3802 KiB

Open AccessArticle

A Comparative Study of Clustering Analysis Method for Driver’s Steering Intention Classification and Identification under Different Typical Conditions

by Yiding Hua, Haobin Jiang, Huan Tian, Xing Xu and Long Chen

Appl. Sci. 2017, 7(10), 1014; https://doi.org/10.3390/app7101014 - 30 Sep 2017

Cited by 16 | Viewed by 5000

Abstract

Driver’s intention classification and identification is identified as the key technology for intelligent vehicles and is widely used in a variety of advanced driver assistant systems (ADAS). To study driver’s steering intention under different typical operating conditions, five driving school coaches of different ages and genders are selected as the test drivers for a real vehicle test. Four kinds of typical car steering condition test data with four different vehicles are collected. Test data are filtered by the Butterworth filter and are used for extracting the driver steering characteristic parameters. Based on Principal Component Analysis (PCA), the three kinds of clustering analysis methods, including the Fuzzy C-Means algorithm (FCM), the Gustafson Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

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653 KiB

Open AccessArticle

Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter

by Kenshi Saho and Masao Masugi

Appl. Sci. 2017, 7(8), 758; https://doi.org/10.3390/app7080758 - 26 Jul 2017

Cited by 6 | Viewed by 4482

Abstract

We present a strategy for designing an

α

β

η

θ

We present a strategy for designing an

α

β

η

θ

filter, a fixed-gain moving-object tracking filter using position and velocity measurements. First, performance indices and stability conditions for the filter are analytically derived. Then, an optimal gain design strategy using these results is proposed and its relationship to the position-velocity-measured (PVM) Kalman filter is shown. Numerical analyses demonstrate the effectiveness of the proposed strategy, as well as a performance improvement over the traditional position-only-measured

α

β

filter. Moreover, we apply an

α

β

η

θ

filter designed using this strategy to ultra-wideband Doppler radar tracking in numerical simulations. We verify that the proposed strategy can easily design the gains for an

α

β

η

θ

filter based on the performance of the ultra-wideband Doppler radar and a rough approximation of the target’s acceleration. Moreover, its effectiveness in predicting the steady state performance in designing the position-velocity-measured Kalman filter is also demonstrated. Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

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7227 KiB

Open AccessArticle

Cellular Automaton to Study the Impact of Changes in Traffic Rules in a Roundabout: A Preliminary Approach

by Krzysztof Małecki and Jarosław Wątróbski

Appl. Sci. 2017, 7(7), 742; https://doi.org/10.3390/app7070742 - 21 Jul 2017

Cited by 24 | Viewed by 6284

Abstract

The current article presents a roundabout traffic model based on cellular automata for computer simulation. The model takes into account various sizes of roundabouts, as well as various types and maximum speeds of vehicles. A realistic vehicle braking phase is presented which is adjusted to the kind of vehicle and weather conditions. It also analyses roundabout traffic options including where the various rules for entering and exiting a roundabout apply. Traffic rules are contained in respective traffic scenarios. The simulation results indicate that there is significant scope for roundabout traffic reorganisation, with a mind to increasing roundabout capacity. Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

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4867 KiB

Open AccessArticle

Adaptive Global Fast Sliding Mode Control for Steer-by-Wire System Road Vehicles

by Junaid Iqbal, Khalil Muhammad Zuhaib, Changsoo Han, Abdul Manan Khan and Mian Ashfaq Ali

Appl. Sci. 2017, 7(7), 738; https://doi.org/10.3390/app7070738 - 19 Jul 2017

Cited by 24 | Viewed by 6216

Abstract

A steer-by-wire (SbW) system, also known as a next-generation steering system, is one of the core elements of autonomous driving technology. Navigating a SbW system road vehicle in varying driving conditions requires an adaptive and robust control scheme to effectively compensate for the uncertain parameter variations and external disturbances. Therefore, this article proposed an adaptive global fast sliding mode control (AGFSMC) for SbW system vehicles with unknown steering parameters. First, the cooperative adaptive sliding mode observer (ASMO) and Kalman filter (KF) are established to simultaneously estimate the vehicle states and cornering stiffness coefficients. Second, based on the best set of estimated dynamics, the AGFSMC is designed to stabilize the impact of nonlinear tire-road disturbance forces and at the same time to estimate the uncertain SbW system parameters. Due to the robust nature of the proposed scheme, it can not only handle the tire–road variation, but also intelligently adapts to the different driving conditions and ensures that the tracking error and the sliding surface converge asymptotically to zero in a finite time. Finally, simulation results and comparative study with other control techniques validate the excellent performance of the proposed scheme. Full article

(This article belongs to the Special Issue Road Vehicles Surroundings Supervision: On-Board Sensors and Communications)

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