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Special Issue "Intelligent Vehicles"

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

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editors

Guest Editor
Prof. Dr. David Fernández-Llorca

Computer Engineering Department, Polytechnic School, University of Alcalá, Campus Universitario s/n, 288805, Alcalá de Henares, Madrid, Spain
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Guest Editor
Prof. Dr. Ignacio Parra Alonso

Computer Engineering Department, Polytechnic School, University of Alcalá, Campus Universitario s/n, 288805, Alcalá de Henares, Madrid, Spain
Website | E-Mail
Guest Editor
Prof. Dr. Iván García Daza

Computer Engineering Department, Polytechnic School, University of Alcalá, Campus Universitario s/n, 288805, Alcalá de Henares, Madrid, Spain
Website | E-Mail
Guest Editor
Prof. Dr. Noelia Hernández Parra

Assistant professor, Computer Engineering Department. INVETT Research Group. Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Website | E-Mail
Interests: Accurate Indoor and Outdoor Global Positioning; Vehicle Localization; Autonomous Vehicles; Driver Assistance Systems; Imaging and Image Analysis

Special Issue Information

Dear Colleagues,

When we talk about intelligent vehicles or driverless cars, we can (almost) state that the future is now. Both industry and academy have made tremendous advancements in the last decade in this field, and a considerable number of prototypes are now autonomously driving our roads. Technology and research findings are moving quickly, and the race is on to develop intelligent vehicles that enable everyone to enjoy safe, efficient, and sustainable mobility.

The capability of intelligent vehicles to sense, interpret, and fully understand the current traffic scene, as well as to infer future states and potential hazards, maybe the main challenge in the driverless cars’ arena. Current scene understanding technologies and methodologies depend on multiple sensor systems, such as cameras (visible or infrared spectrum), radar, LiDAR, and so on, and are based on highly complex and sophisticated algorithms, including artificial intelligence. New approaches to model the behavior of other road users (VRUs and drivers) are needed in order to ensure the safety of the control strategies. Robust sensing under different lighting and weather conditions becomes mandatory to advance towards fail-aware, fail-safe, and fail operational systems.

The aim of this Special Issue is to contribute to the state-of-the-art, and to introduce current developments concerning the perception and sensor technologies for intelligent vehicles. We encourage potential authors to submit contributions of original research, new developments, and substantial experimental works concerning intelligent vehicles. Surveys are very welcomed too.

Therefore, prospective authors are invited to submit original contributions or survey papers for review for publication in the Sensors open access journal. Topics of interest include (but are not limited to) the following:

  • Sensor technologies for driverless cars
  • Vehicle scene understanding
  • Vulnerable road users (VRUs) protection
  • Vehicle navigation and localization systems
  • Advanced driver assistance systems
  • Intelligent vehicles related image, radar, and LiDAR signal processing
  • Sensor and information fusion
  • Human factors and human machine interaction
  • Assistive intelligent vehicles
  • Driver and road users state and intent recognition
  • Cooperative driving
  • Sensing under different lighting and weather conditions
  • Fail-safe, fail-aware, and fail-operational systems
  • HD and accurate mapping systems

Prof. Dr. David Fernández-Llorca
Prof. Dr. Ignacio Parra Alonso
Prof. Dr. Iván García Daza
Prof. Dr. Noelia Hernández Parra
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Intelligent vehicles
  • Sensors
  • Road users behavior modeling
  • Sensor and information fusion
  • Advanced driver assistance systems
  • Image, radar, and LiDAR signal processing
  • Human factors
  • Fail-safe, fail-aware, and fail-operational

Published Papers (9 papers)

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Research

Open AccessArticle
Real-Time Photometric Calibrated Monocular Direct Visual SLAM
Sensors 2019, 19(16), 3604; https://doi.org/10.3390/s19163604
Received: 28 June 2019 / Revised: 12 August 2019 / Accepted: 16 August 2019 / Published: 19 August 2019
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Abstract
To solve the illumination sensitivity problems of mobile ground equipment, an enhanced visual SLAM algorithm based on the sparse direct method was proposed in this paper. Firstly, the vignette and response functions of the input sequences were optimized based on the photometric formation [...] Read more.
To solve the illumination sensitivity problems of mobile ground equipment, an enhanced visual SLAM algorithm based on the sparse direct method was proposed in this paper. Firstly, the vignette and response functions of the input sequences were optimized based on the photometric formation of the camera. Secondly, the Shi–Tomasi corners of the input sequence were tracked, and optimization equations were established using the pixel tracking of sparse direct visual odometry (VO). Thirdly, the Levenberg–Marquardt (L–M) method was applied to solve the joint optimization equation, and the photometric calibration parameters in the VO were updated to realize the real-time dynamic compensation of the exposure of the input sequences, which reduced the effects of the light variations on SLAM’s (simultaneous localization and mapping) accuracy and robustness. Finally, a Shi–Tomasi corner filtered strategy was designed to reduce the computational complexity of the proposed algorithm, and the loop closure detection was realized based on the oriented FAST and rotated BRIEF (ORB) features. The proposed algorithm was tested using TUM, KITTI, EuRoC, and an actual environment, and the experimental results show that the positioning and mapping performance of the proposed algorithm is promising. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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Open AccessArticle
Sensor Fault Detection and Signal Restoration in Intelligent Vehicles
Sensors 2019, 19(15), 3306; https://doi.org/10.3390/s19153306
Received: 5 June 2019 / Revised: 23 July 2019 / Accepted: 24 July 2019 / Published: 27 July 2019
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Abstract
This paper presents fault diagnosis logic and signal restoration algorithms for vehicle motion sensors. Because various sensors are equipped to realize automatic operation of the vehicle, defects in these sensors lead to severe safety issues. Therefore, an effective and reliable fault detection and [...] Read more.
This paper presents fault diagnosis logic and signal restoration algorithms for vehicle motion sensors. Because various sensors are equipped to realize automatic operation of the vehicle, defects in these sensors lead to severe safety issues. Therefore, an effective and reliable fault detection and recovery system should be developed. The primary idea of the proposed fault detection system is the conversion of measured wheel speeds into vehicle central axis information and the selection of a reference central axis speed based on this information. Thus, the obtained results are employed to estimate the speed for all wheel sides, which are compared with measured values to identify fault and recover the fault signal. For fault diagnosis logic, a conditional expression is derived with only two variables to distinguish between normal and fault; further, an analytical redundancy structure and a simple diagnostic logic structure are presented. Finally, an off-line test is conducted using test vehicle information to validate the proposed method; it demonstrates that the proposed fault detection and signal restoration algorithm can satisfy the control performance required for each sensor failure. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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Open AccessArticle
Vehicle Driver Monitoring through the Statistical Process Control
Sensors 2019, 19(14), 3059; https://doi.org/10.3390/s19143059
Received: 23 April 2019 / Revised: 22 June 2019 / Accepted: 9 July 2019 / Published: 11 July 2019
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Abstract
This paper proposes the use of the Statistical Process Control (SPC), more specifically, the Exponentially Weighted Moving Average method, for the monitoring of drivers using approaches based on the vehicle and the driver’s behavior. Based on the SPC, we propose a method for [...] Read more.
This paper proposes the use of the Statistical Process Control (SPC), more specifically, the Exponentially Weighted Moving Average method, for the monitoring of drivers using approaches based on the vehicle and the driver’s behavior. Based on the SPC, we propose a method for the lane departure detection; a method for detecting sudden driver movements; and a method combined with computer vision to detect driver fatigue. All methods consider information from sensors scattered by the vehicle. The results showed the efficiency of the methods in the identification and detection of unwanted driver actions, such as sudden movements, lane departure, and driver fatigue. Lane departure detection obtained results of up to 76.92% (without constant speed) and 84.16% (speed maintained at ≈60). Furthermore, sudden movements detection obtained results of up to 91.66% (steering wheel) and 94.44% (brake). The driver fatigue has been detected in up to 94.46% situations. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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Open AccessArticle
A Strain-Based Method to Estimate Tire Parameters for Intelligent Tires under Complex Maneuvering Operations
Sensors 2019, 19(13), 2973; https://doi.org/10.3390/s19132973
Received: 30 April 2019 / Revised: 19 June 2019 / Accepted: 2 July 2019 / Published: 5 July 2019
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Abstract
The possibility of using tires as active sensors opens the door to a huge number of different ways to accomplish this goal. In this case, based on a tire equipped with strain sensors, also known as an Intelligent Tire, relevant vehicle dynamics information [...] Read more.
The possibility of using tires as active sensors opens the door to a huge number of different ways to accomplish this goal. In this case, based on a tire equipped with strain sensors, also known as an Intelligent Tire, relevant vehicle dynamics information can be provided. The purpose of this research is to improve the strain-based methodology for Intelligent Tires to estimate all tire forces, based only on deformations measured in the contact patch. Firstly, through an indoor test rig data, an algorithm has been developed to pick out the relevant features of strain data and correlate them with tire parameters. This information of the tire contact patch is then transmitted to a fuzzy logic system to estimate the tire parameters. To evaluate the reliability of the proposed estimator, the well-known simulation software CarSim has been used to back up the estimation results. The software CarSim has been used to provide the vehicle parameters in complex maneuvers. Finally, the estimations have been checked with the simulation results. This approach has enabled the behaviour of the intelligent tire to be tested for different maneuvers and velocities, providing key information about the tire parameters directly from the only contact that exists between the vehicle and the road. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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Open AccessArticle
Semantic Segmentation with Transfer Learning for Off-Road Autonomous Driving
Sensors 2019, 19(11), 2577; https://doi.org/10.3390/s19112577
Received: 2 April 2019 / Revised: 24 May 2019 / Accepted: 30 May 2019 / Published: 6 June 2019
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Abstract
Since the state-of-the-art deep learning algorithms demand a large training dataset, which is often unavailable in some domains, the transfer of knowledge from one domain to another has been a trending technique in the computer vision field. However, this method may not be [...] Read more.
Since the state-of-the-art deep learning algorithms demand a large training dataset, which is often unavailable in some domains, the transfer of knowledge from one domain to another has been a trending technique in the computer vision field. However, this method may not be a straight-forward task considering several issues such as original network size or large differences between the source and target domain. In this paper, we perform transfer learning for semantic segmentation of off-road driving environments using a pre-trained segmentation network called DeconvNet. We explore and verify two important aspects regarding transfer learning. First, since the original network size was very large and did not perform well for our application, we proposed a smaller network, which we call the light-weight network. This light-weight network is half the size to the original DeconvNet architecture. We transferred the knowledge from the pre-trained DeconvNet to our light-weight network and fine-tuned it. Second, we used synthetic datasets as the intermediate domain before training with the real-world off-road driving data. Fine-tuning the model trained with the synthetic dataset that simulates the off-road driving environment provides more accurate results for the segmentation of real-world off-road driving environments than transfer learning without using a synthetic dataset does, as long as the synthetic dataset is generated considering real-world variations. We also explore the issue whereby the use of a too simple and/or too random synthetic dataset results in negative transfer. We consider the Freiburg Forest dataset as a real-world off-road driving dataset. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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Open AccessArticle
Multi-Stage Hough Space Calculation for Lane Markings Detection via IMU and Vision Fusion
Sensors 2019, 19(10), 2305; https://doi.org/10.3390/s19102305
Received: 13 April 2019 / Revised: 11 May 2019 / Accepted: 14 May 2019 / Published: 19 May 2019
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Abstract
It is challenging to achieve robust lane detection based on a single frame, particularly when complicated driving scenarios are present. A novel approach based on multiple frames is proposed in this paper by taking advantage of the fusion of vision and Inertial Measurement [...] Read more.
It is challenging to achieve robust lane detection based on a single frame, particularly when complicated driving scenarios are present. A novel approach based on multiple frames is proposed in this paper by taking advantage of the fusion of vision and Inertial Measurement Units (IMU). Hough space is employed as a storage medium where lane markings can be stored and visited conveniently. The detection of lane markings is achieved by the following steps. Firstly, primary line segments are extracted from a basic Hough space, which is calculated by Hough Transform. Secondly, a CNN-based classifier is introduced to measure the confidence probability of each line segment, and transforms the basic Hough space into a probabilistic Hough space. In the third step, pose information provided by the IMU is applied to align previous probabilistic Hough spaces to the current one and a filtered probabilistic Hough space is acquired by smoothing the primary probabilistic Hough space across frames. Finally, valid line segments with probability higher than 0.7 are extracted from the filtered probabilistic Hough space. The proposed approach is applied experimentally, and the results demonstrate a satisfying performance compared to various existing methods. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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Open AccessArticle
Simulating Dynamic Driving Behavior in Simulation Test for Unmanned Vehicles via Multi-Sensor Data
Sensors 2019, 19(7), 1670; https://doi.org/10.3390/s19071670
Received: 25 February 2019 / Revised: 30 March 2019 / Accepted: 3 April 2019 / Published: 8 April 2019
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Abstract
Driving behavior is the main basis for evaluating the performance of an unmanned vehicle. In simulation tests of unmanned vehicles, in order for simulation results to be approximated to the actual results as much as possible, model of driving behaviors must be able [...] Read more.
Driving behavior is the main basis for evaluating the performance of an unmanned vehicle. In simulation tests of unmanned vehicles, in order for simulation results to be approximated to the actual results as much as possible, model of driving behaviors must be able to exhibit actual motion of unmanned vehicles. We propose an automatic approach of simulating dynamic driving behaviors of vehicles in traffic scene represented by image sequences. The spatial topological attributes and appearance attributes of virtual vehicles are computed separately according to the constraint of geometric consistency of sparse 3D space organized by image sequence. To achieve this goal, we need to solve three main problems: Registration of vehicle in a 3D space of road environment, vehicle’s image observed from corresponding viewpoint in the road scene, and consistency of the vehicle and the road environment. After the proposed method was embedded in a scene browser, a typical traffic scene including the intersections was chosen for a virtual vehicle to execute the driving tasks of lane change, overtaking, slowing down and stop, right turn, and U-turn. The experimental results show that different driving behaviors of vehicles in typical traffic scene can be exhibited smoothly and realistically. Our method can also be used for generating simulation data of traffic scenes that are difficult to collect. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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Open AccessArticle
Portable System for Monitoring and Controlling Driver Behavior and the Use of a Mobile Phone While Driving
Sensors 2019, 19(7), 1563; https://doi.org/10.3390/s19071563
Received: 25 February 2019 / Revised: 27 March 2019 / Accepted: 28 March 2019 / Published: 31 March 2019
Cited by 2 | PDF Full-text (9826 KB) | HTML Full-text | XML Full-text
Abstract
There is an utmost requirement for technology to control a driver’s phone while driving, which will prevent the driver from being distracted and thus saving the driver’s and passenger’s lives. Information from recent studies has shown that 70% of the young and aware [...] Read more.
There is an utmost requirement for technology to control a driver’s phone while driving, which will prevent the driver from being distracted and thus saving the driver’s and passenger’s lives. Information from recent studies has shown that 70% of the young and aware drivers are used to texting while driving. There are many different technologies used to control mobile phones while driving, including electronic device control, global positioning system (GPS), on-board diagnostics (OBD)-II-based devices, mobile phone applications or apps, etc. These devices acquire the vehicle information such as the car speed and use the information to control the driver’s phone such as preventing them from making or receiving calls at specific speed limits. The information from the devices is interfaced via Bluetooth and can later be used to control mobile phone applications. The main aim of this paper is to propose the design of a portable system for monitoring the use of a mobile phone while driving and for controlling a driver’s mobile phone, if necessary, when the vehicle reaches a specific speed limit (>10 km/h). A paper-based self-reported questionnaire survey was carried out among 600 teenage drivers from different nationalities to see the driving behavior of young drivers in Qatar. Finally, a mobile application was developed to monitor the mobile usage of a driver and an OBD-II module-based portable system was designed to acquire data from the vehicle to identify drivers’ behavior with respect to phone usage, sudden lane changes, and abrupt breaking/sharp speeding. This information was used in a mobile application to control the driver’s mobile usage as well as to report the driving behavior while driving. The application of such a system can significantly improve drivers’ behavior all over the world. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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Open AccessArticle
Multi-Vehicle Tracking via Real-Time Detection Probes and a Markov Decision Process Policy
Sensors 2019, 19(6), 1309; https://doi.org/10.3390/s19061309
Received: 25 January 2019 / Revised: 6 March 2019 / Accepted: 9 March 2019 / Published: 15 March 2019
Cited by 1 | PDF Full-text (3231 KB) | HTML Full-text | XML Full-text
Abstract
Online multi-object tracking (MOT) has broad applications in time-critical video analysis scenarios such as advanced driver-assistance systems (ADASs) and autonomous driving. In this paper, the proposed system aims at tracking multiple vehicles in the front view of an onboard monocular camera. The vehicle [...] Read more.
Online multi-object tracking (MOT) has broad applications in time-critical video analysis scenarios such as advanced driver-assistance systems (ADASs) and autonomous driving. In this paper, the proposed system aims at tracking multiple vehicles in the front view of an onboard monocular camera. The vehicle detection probes are customized to generate high precision detection, which plays a basic role in the following tracking-by-detection method. A novel Siamese network with a spatial pyramid pooling (SPP) layer is applied to calculate pairwise appearance similarity. The motion model captured from the refined bounding box provides the relative movements and aspects. The online-learned policy treats each tracking period as a Markov decision process (MDP) to maintain long-term, robust tracking. The proposed method is validated in a moving vehicle with an onboard NVIDIA Jetson TX2 and returns real-time speeds. Compared with other methods on KITTI and self-collected datasets, our method achieves significant performance in terms of the “Mostly-tracked”, “Fragmentation”, and “ID switch” variables. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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