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Sensors for Road Vehicles of the Future
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Sensors for Road Vehicles of the Future

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

Deadline for manuscript submissions: closed (15 January 2021) | Viewed by 64096

Special Issue Editor

Dr. Felipe Jiménez

grade E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New vehicles include several systems that improve their safety, comfort, and performance. A key part of these systems is the use of several sensors around the vehicle, capturing information from the vehicle and its surroundings. For this reason, today, the development and implementation of new sensors is crucial, using new technologies, improving their measuring capabilities, and providing new information that, up to now, has not been necessary but has become essential.

This Special Issue deals with sensors that have been introduced or will be introduced in the near future in road vehicles. Several sensor families are included in this group, such as the following: propulsion system sensors, sensors for assistance systems, sensors for vehicle dynamics, sensors for capturing information from the vehicle’s surroundings, sensors for capturing data from the vehicle interior, sensors for driver supervision, etc. Positioning and digital maps could also be considered as secondary sensors that could provide information, and thus, their challenges will also be taken into account.

Furthermore, new technologies for sensors are now appearing in order to overcome current limitations, to provide new services that had not previously been considered, or to mitigate the high costs of the relevant technology.

Finally, new sensors involve new algorithms to be implemented for new systems. In this field, we could include perception algorithms (for example, road or obstacle detection) and control algorithms for assistance or autonomous applications. In many cases, algorithms involve sensor fusion, and current trends and solutions in this field are also a key issue in obtaining reliable and complete information.

Similarly, although the scope of this Special Issue is not specifically focused on the final systems, practical applications supported by the new sensors may also be included.

Issues related to the applicable requirements for sensors to meet the specifications of new systems are also included within the scope of this Special Issue. In this regard, it is relevant to indicate the specific requirements that must be taken into account in the automotive sector, given the strong accuracy, availability, and reliability of specifications. Moreover, the harsh environment in which they work (noise, vibration, dirt, etc.) must be considered.

Finally, studies of the state of the art in relation to the evolution of onboard sensors on vehicles and their impact on the evolution of the automobile are also welcome.

In conclusion, this Special Issue aims to bring together innovative developments in areas related to sensors and smart cities, including, but not limited to, the following:

  • Sensors;
  • Engine sensors;
  • Perception sensors;
  • Vehicle dynamics sensors;
  • Sensors for driver supervision;
  • Positioning and digital maps;
  • New assistance systems based on new sensors;
  • Sensorial technologies;
  • Sensors for connected and autonomous driving;
  • Sensors requirements;
  • Review of the state of the art of sensors in road vehicles.

Authors are invited to contact the guest editor prior to submission if they are uncertain about whether their work falls within the general scope of this Special Issue.

Prof. Felipe Jiménez
Guest Editor

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 submissions that pass pre-check are 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 2600 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

  • Road vehicles
  • Sensors
  • Engine
  • Positioning
  • Sensor fusion
  • Perception sensors
  • Vehicle dynamics sensors
  • Driver assistance systems
  • Connected and autonomous vehicles

Published Papers (16 papers)

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Editorial

Jump to: Research

3 pages, 199 KiB  
Editorial
Sensors for Road Vehicles of the Future
by Felipe Jiménez
Sensors 2023, 23(1), 22; https://doi.org/10.3390/s23010022 - 20 Dec 2022
Cited by 2 | Viewed by 1017
Abstract
New vehicles include several systems that improve their safety, comfort, and performance [...] Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)

Research

Jump to: Editorial

17 pages, 1296 KiB  
Article
Autonomous Ground Vehicle Lane-Keeping LPV Model-Based Control: Dual-Rate State Estimation and Comparison of Different Real-Time Control Strategies
by Julián M. Salt Ducajú, Julián J. Salt Llobregat, Ángel Cuenca and Masayoshi Tomizuka
Sensors 2021, 21(4), 1531; https://doi.org/10.3390/s21041531 - 23 Feb 2021
Cited by 21 | Viewed by 3178
Abstract
In this contribution, we suggest two proposals to achieve fast, real-time lane-keeping control for Autonomous Ground Vehicles (AGVs). The goal of lane-keeping is to orient and keep the vehicle within a given reference path using the front wheel steering angle as the control [...] Read more.
In this contribution, we suggest two proposals to achieve fast, real-time lane-keeping control for Autonomous Ground Vehicles (AGVs). The goal of lane-keeping is to orient and keep the vehicle within a given reference path using the front wheel steering angle as the control action for a specific longitudinal velocity. While nonlinear models can describe the lateral dynamics of the vehicle in an accurate manner, they might lead to difficulties when computing some control laws such as Model Predictive Control (MPC) in real time. Therefore, our first proposal is to use a Linear Parameter Varying (LPV) model to describe the AGV’s lateral dynamics, as a trade-off between computational complexity and model accuracy. Additionally, AGV sensors typically work at different measurement acquisition frequencies so that Kalman Filters (KFs) are usually needed for sensor fusion. Our second proposal is to use a Dual-Rate Extended Kalman Filter (DREFKF) to alleviate the cost of updating the internal state of the filter. To check the validity of our proposals, an LPV model-based control strategy is compared in simulations over a circuit path to another reduced computational complexity control strategy, the Inverse Kinematic Bicycle model (IKIBI), in the presence of process and measurement Gaussian noise. The LPV-MPC controller is shown to provide a more accurate lane-keeping behavior than an IKIBI control strategy. Finally, it is seen that Dual-Rate Extended Kalman Filters (DREKFs) constitute an interesting tool for providing fast vehicle state estimation in an AGV lane-keeping application. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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22 pages, 5840 KiB  
Article
Sensors on the Move: Onboard Camera-Based Real-Time Traffic Alerts Paving the Way for Cooperative Roads
by Olatz Iparraguirre, Aiert Amundarain, Alfonso Brazalez and Diego Borro
Sensors 2021, 21(4), 1254; https://doi.org/10.3390/s21041254 - 10 Feb 2021
Cited by 6 | Viewed by 2546
Abstract
European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and [...] Read more.
European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission’s road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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21 pages, 8141 KiB  
Article
A Novel Method for Determining Angular Speed and Acceleration Using Sin-Cos Encoders
by Manuel Alcázar Vargas, Javier Pérez Fernández, Juan M. Velasco García, Juan A. Cabrera Carrillo and Juan J. Castillo Aguilar
Sensors 2021, 21(2), 577; https://doi.org/10.3390/s21020577 - 15 Jan 2021
Cited by 8 | Viewed by 3335
Abstract
The performance of vehicle safety systems depends very much on the accuracy of the signals coming from vehicle sensors. Among them, the wheel speed is of vital importance. This paper describes a new method to obtain the wheel speed by using Sin-Cos encoders. [...] Read more.
The performance of vehicle safety systems depends very much on the accuracy of the signals coming from vehicle sensors. Among them, the wheel speed is of vital importance. This paper describes a new method to obtain the wheel speed by using Sin-Cos encoders. The methodology is based on the use of the Savitzky–Golay filters to optimally determine the coefficients of the polynomials that best fit the measured signals and their time derivatives. The whole process requires a low computational cost, which makes it suitable for real-time applications. This way it is possible to provide the safety system with an accurate measurement of both the angular speed and acceleration of the wheels. The proposed method has been compared to other conventional approaches. The results obtained in simulations and real tests show the superior performance of the proposed method, particularly for medium and low wheel angular speeds. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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29 pages, 1235 KiB  
Article
Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration
by Máté Fazekas, Péter Gáspár and Balázs Németh
Sensors 2021, 21(2), 337; https://doi.org/10.3390/s21020337 - 06 Jan 2021
Cited by 15 | Viewed by 3522
Abstract
Localization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. [...] Read more.
Localization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. This paper proposes a novel wheel odometry model and its calibration. The parameters of the nonlinear dynamic system are estimated with Gauss–Newton regression. Due to only automotive-grade sensors are applied to reach a cost-effective system, the measurement uncertainty highly corrupts the estimation accuracy. The problem is handled with a unique Kalman-filter addition to the iterative loop. The experimental results illustrate that without the proposed improvements, in particular the dynamic wheel assumption and integrated filtering, the model cannot be calibrated precisely. With the well-calibrated odometry, the localization accuracy improves significantly and the system can be used as a cost-effective motion estimation sensor in autonomous functions. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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15 pages, 6678 KiB  
Article
A Novel Algorithm for Detecting Pedestrians on Rainy Image
by Yuhang Liu, Jianxiao Ma, Yuchen Wang and Chenhong Zong
Sensors 2021, 21(1), 112; https://doi.org/10.3390/s21010112 - 27 Dec 2020
Cited by 11 | Viewed by 2492
Abstract
Pedestrian detection is widely used in cooperative vehicle infrastructure systems. Traditional pedestrian detection methods perform sufficiently well under sunny scenarios and obtain trustworthy traffic data. However, the detection drastically decreases under rainy scenarios. This study proposes a pedestrian detection algorithm with a de-raining [...] Read more.
Pedestrian detection is widely used in cooperative vehicle infrastructure systems. Traditional pedestrian detection methods perform sufficiently well under sunny scenarios and obtain trustworthy traffic data. However, the detection drastically decreases under rainy scenarios. This study proposes a pedestrian detection algorithm with a de-raining module that improves detection accuracy under various rainy scenarios. Specifically, this algorithm determines the density information of rain and effectively removes rain streaks through the de-raining module. Then the algorithm detects pedestrians as a pair of keypoints through the pedestrian detection module to solve the problem of occlusion. Furthermore, a new pedestrian dataset containing rain density labels is established and used to train the algorithm. For the scenarios of light, medium, and heavy rain, extensive experiments on synthetic datasets demonstrate that the proposed algorithm increases AP (average precision) of pedestrian detection by 21.1%, 48.1%, and 60.9%. Moreover, the proposed algorithm performs well on real datasets and achieves improvements over the state-of-the-art methods, which reveals that the proposed algorithm can significantly improve the accuracy of pedestrian detection in rainy scenarios. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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15 pages, 4680 KiB  
Article
A Hybrid Active Noise Control System for the Attenuation of Road Noise Inside a Vehicle Cabin
by Zibin Jia, Xu Zheng, Quan Zhou, Zhiyong Hao and Yi Qiu
Sensors 2020, 20(24), 7190; https://doi.org/10.3390/s20247190 - 15 Dec 2020
Cited by 19 | Viewed by 2994
Abstract
This paper proposed a local active control method for the reduction of road noise inside a vehicle cabin. A multichannel simplified hybrid active noise control (sHANC) system was first developed and applied to the rear left seat of a large sport utility vehicle [...] Read more.
This paper proposed a local active control method for the reduction of road noise inside a vehicle cabin. A multichannel simplified hybrid active noise control (sHANC) system was first developed and applied to the rear left seat of a large sport utility vehicle (SUV). The attenuation capability of the sHANC system was investigated through simulations, using reference signals provided by accelerometers on the suspensions and bodywork of the vehicle and microphones on the floor of cabin, respectively. It was shown that compared to the traditional feedforward system, the sHANC system using either vibrational or acoustical reference signals can produce a significant suppression of the narrowband peak noise between 75 and 80 Hz, but the system lost the control capability in a range of 100–500 Hz when the acoustic signals were used as references. To reduce the practical implementation costs while maintaining excellent reduction performance, a modified simplified hybrid ANC (msHANC) system was further proposed, in which combined vibrational and acoustical signals were used as reference signals. The off-line analyses showed that four reference accelerometers can be substituted by ten microphones without compromising attenuation performance, with 3.7 dBA overall noise reduction being achieved. The effect of delays on the reduction performance of msHANC system was also investigated. The result showed that the msHANC system was more sensitive to the delays compared to the sHANC system if using only vibrational reference signals. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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14 pages, 4326 KiB  
Article
Lateral Position Measurement Based on Vehicles’ Longitudinal Displacement
by Ibrahim Mohsen, Thierry Ditchi, Stéphane Holé and Emmanuel Géron
Sensors 2020, 20(24), 7183; https://doi.org/10.3390/s20247183 - 15 Dec 2020
Cited by 3 | Viewed by 2141
Abstract
The lateral position of a vehicle in its lane is crucial information required to develop intelligent assistant driving systems. Current studies reveal this information by mixing multiple sources such as cameras, LiDAR or accurate GNSS. Because these systems are not efficient in some [...] Read more.
The lateral position of a vehicle in its lane is crucial information required to develop intelligent assistant driving systems. Current studies reveal this information by mixing multiple sources such as cameras, LiDAR or accurate GNSS. Because these systems are not efficient in some degraded weather conditions, a cooperative Vehicle-to-Infrastructure sensor has been developed to help to determine lateral position of a vehicle in its lane. In this paper, the authors propose a completely new and original way to estimate lateral position of the vehicle in its lane using the longitudinal displacement. Using a system based on a hyper-frequency interaction between a transceiver module embedded in the vehicle and passive transponders that can be integrated in the road, for instance under the lane markings, a new signal processing algorithm is presented in order to determine the lateral distance between the vehicle and the transponder axis. The sensor has been tested in an external environment and has shown an estimated lateral distance error of 8 cm at most. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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19 pages, 4516 KiB  
Article
5G Beyond 3GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
by Gorka Velez, Ángel Martín, Giancarlo Pastor and Edward Mutafungwa
Sensors 2020, 20(22), 6622; https://doi.org/10.3390/s20226622 - 19 Nov 2020
Cited by 21 | Viewed by 3142
Abstract
Fifth-generation (5G) mobile networks aim to be qualified as the core connectivity infrastructures to address connected automated mobility (CAM), both from a technological and from a business perspective, for the higher automation levels defined by the automotive industry. Specifically, in some territories such [...] Read more.
Fifth-generation (5G) mobile networks aim to be qualified as the core connectivity infrastructures to address connected automated mobility (CAM), both from a technological and from a business perspective, for the higher automation levels defined by the automotive industry. Specifically, in some territories such as the European Union the cross-border corridors have relevance, as they are the cohesive paths for terrestrial transport. Therefore, 5G for CAM applications is planned to be deployed there first. However, cross-border contexts imply paramount communication challenges, such as seamless roaming, not addressed by current technology. This paper identifies relevant future 5G enhancements, specifically those specified by Third-Generation Partnership Project (3GPP) releases beyond Release 15, and outlines how they will support the ambitions of highly automated driving in cross-border corridors. In order to conduct this study, a set of representative use cases and the related communication requirements were identified. Then, for each use case, the most relevant 5G features were proposed. Some open issues are described at the end. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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31 pages, 11210 KiB  
Article
Drive-By-Wire Development Process Based on ROS for an Autonomous Electric Vehicle
by J. Felipe Arango, Luis M. Bergasa, Pedro A. Revenga, Rafael Barea, Elena López-Guillén, Carlos Gómez-Huélamo, Javier Araluce and Rodrigo Gutiérrez
Sensors 2020, 20(21), 6121; https://doi.org/10.3390/s20216121 - 27 Oct 2020
Cited by 14 | Viewed by 6953
Abstract
This paper presents the development process of a robust and ROS-based Drive-By-Wire system designed for an autonomous electric vehicle from scratch over an open source chassis. A revision of the vehicle characteristics and the different modules of our navigation architecture is carried out [...] Read more.
This paper presents the development process of a robust and ROS-based Drive-By-Wire system designed for an autonomous electric vehicle from scratch over an open source chassis. A revision of the vehicle characteristics and the different modules of our navigation architecture is carried out to put in context our Drive-by-Wire system. The system is composed of a Steer-By-Wire module and a Throttle-By-Wire module that allow driving the vehicle by using some commands of lineal speed and curvature, which are sent through a local network from the control unit of the vehicle. Additionally, a Manual/Automatic switching system has been implemented, which allows the driver to activate the autonomous driving and safely taking control of the vehicle at any time. Finally, some validation tests were performed for our Drive-By-Wire system, as a part of our whole autonomous navigation architecture, showing the good working of our proposal. The results prove that the Drive-By-Wire system has the behaviour and necessary requirements to automate an electric vehicle. In addition, after 812 h of testing, it was proven that it is a robust Drive-By-Wire system, with high reliability. The developed system is the basis for the validation and implementation of new autonomous navigation techniques developed within the group in a real vehicle. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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21 pages, 3536 KiB  
Article
Low-Cost Road-Surface Classification System Based on Self-Organizing Maps
by Ignacio Sánchez Andrades, Juan J. Castillo Aguilar, Juan M. Velasco García, Juan A. Cabrera Carrillo and Miguel Sánchez Lozano
Sensors 2020, 20(21), 6009; https://doi.org/10.3390/s20216009 - 23 Oct 2020
Cited by 10 | Viewed by 2863
Abstract
Expanding the performance and autonomous-decision capability of driver-assistance systems is critical in today’s automotive engineering industry to help drivers and reduce accident incidence. It is essential to provide vehicles with the necessary perception systems, but without creating a prohibitively expensive product. In this [...] Read more.
Expanding the performance and autonomous-decision capability of driver-assistance systems is critical in today’s automotive engineering industry to help drivers and reduce accident incidence. It is essential to provide vehicles with the necessary perception systems, but without creating a prohibitively expensive product. In this area, the continuous and precise estimation of a road surface on which a vehicle moves is vital for many systems. This paper proposes a low-cost approach to solve this issue. The developed algorithm resorts to analysis of vibrations generated by the tyre-rolling movement to classify road surfaces, which allows for optimizing vehicular-safety-system performance. The signal is analyzed by means of machine-learning techniques, and the classification and estimation of the surface are carried out with the use of a self-organizing-map (SOM) algorithm. Real recordings of the vibration produced by tyre rolling on six different types of surface were used to generate the model. The efficiency of the proposed model (88.54%) and its speed of execution were compared with those of other classifiers in order to evaluate its performance. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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35 pages, 37123 KiB  
Article
System, Design and Experimental Validation of Autonomous Vehicle in an Unconstrained Environment
by Shoaib Azam, Farzeen Munir, Ahmad Muqeem Sheri, Joonmo Kim and Moongu Jeon
Sensors 2020, 20(21), 5999; https://doi.org/10.3390/s20215999 - 22 Oct 2020
Cited by 16 | Viewed by 8472
Abstract
In recent years, technological advancements have made a promising impact on the development of autonomous vehicles. The evolution of electric vehicles, development of state-of-the-art sensors, and advances in artificial intelligence have provided necessary tools for the academia and industry to develop the prototypes [...] Read more.
In recent years, technological advancements have made a promising impact on the development of autonomous vehicles. The evolution of electric vehicles, development of state-of-the-art sensors, and advances in artificial intelligence have provided necessary tools for the academia and industry to develop the prototypes of autonomous vehicles that enhance the road safety and traffic efficiency. The increase in the deployment of sensors for the autonomous vehicle, make it less cost-effective to be utilized by the consumer. This work focuses on the development of full-stack autonomous vehicle using the limited amount of sensors suite. The architecture aspect of the autonomous vehicle is categorized into four layers that include sensor layer, perception layer, planning layer and control layer. In the sensor layer, the integration of exteroceptive and proprioceptive sensors on the autonomous vehicle are presented. The perception of the environment in term localization and detection using exteroceptive sensors are included in the perception layer. In the planning layer, algorithms for mission and motion planning are illustrated by incorporating the route information, velocity replanning and obstacle avoidance. The control layer constitutes lateral and longitudinal control for the autonomous vehicle. For the verification of the proposed system, the autonomous vehicle is tested in an unconstrained environment. The experimentation results show the efficacy of each module, including localization, object detection, mission and motion planning, obstacle avoidance, velocity replanning, lateral and longitudinal control. Further, in order to demonstrate the experimental validation and the application aspect of the autonomous vehicle, the proposed system is tested as an autonomous taxi service. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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15 pages, 8650 KiB  
Article
End-to-End Monocular Range Estimation for Forward Collision Warning
by Jie Tang and Jian Li
Sensors 2020, 20(20), 5941; https://doi.org/10.3390/s20205941 - 21 Oct 2020
Cited by 6 | Viewed by 2823
Abstract
Estimating range to the closest object in front is the core component of the forward collision warning (FCW) system. Previous monocular range estimation methods mostly involve two sequential steps of object detection and range estimation. As a result, they are only effective for [...] Read more.
Estimating range to the closest object in front is the core component of the forward collision warning (FCW) system. Previous monocular range estimation methods mostly involve two sequential steps of object detection and range estimation. As a result, they are only effective for objects from specific categories relying on expensive object-level annotation for training, but not for unseen categories. In this paper, we present an end-to-end deep learning architecture to solve the above problems. Specifically, we represent the target range as a weighted sum of a set of potential distances. These potential distances are generated by inverse perspective projection based on intrinsic and extrinsic camera parameters, while a deep neural network predicts the corresponding weights of these distances. The whole architecture is optimized towards the range estimation task directly in an end-to-end manner with only the target range as supervision. As object category is not restricted in the training stage, the proposed method can generalize to objects with unseen categories. Furthermore, camera parameters are explicitly considered in the proposed method, making it able to generalize to images taken with different cameras and novel views. Additionally, the proposed method is not a pure black box, but provides partial interpretability by visualizing the produced weights to see which part of the image dominates the final result. We conduct experiments to verify the above properties of the proposed method on synthetic and real-world collected data. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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19 pages, 1589 KiB  
Article
Driver Monitoring for a Driver-Centered Design and Assessment of a Merging Assistance System Based on V2V Communications
by Sofia Sánchez–Mateo, Elisa Pérez–Moreno and Felipe Jiménez
Sensors 2020, 20(19), 5582; https://doi.org/10.3390/s20195582 - 29 Sep 2020
Cited by 7 | Viewed by 2891
Abstract
Merging is one of the most critical scenarios that can be found in road transport. In this maneuver, the driver is subjected to a high mental load due to the large amount of information he handles, while making decisions becomes a crucial issue [...] Read more.
Merging is one of the most critical scenarios that can be found in road transport. In this maneuver, the driver is subjected to a high mental load due to the large amount of information he handles, while making decisions becomes a crucial issue for their safety and those in adjacent vehicles. In previous works, it was studied how the merging maneuver affected the cognitive load required for driving by means of an eye tracking system, justifying the proposal of a driver assistance system for the merging maneuver on highways. This paper presents a merging assistance system based on communications between vehicles, which allows vehicles to share internal variables of position and speed and is implemented on a mobile device located inside the vehicle. The system algorithm decides where and when the vehicle can start the merging maneuver in safe conditions and provides the appropriate information to the driver. Parameters and driving simulator tests are used for the interface definition to develop the less intrusive and demanding one. Afterward, the system prototype was installed in a real passenger car and tests in real scenarios were conducted with several drivers to assess usability and mental load. Comparisons among alternative solutions are shown and effectiveness is assessed. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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28 pages, 30314 KiB  
Article
Improved Dynamic Obstacle Mapping (iDOMap)
by Ángel Llamazares, Eduardo Molinos, Manuel Ocaña and Vladimir Ivan
Sensors 2020, 20(19), 5520; https://doi.org/10.3390/s20195520 - 26 Sep 2020
Cited by 3 | Viewed by 3741
Abstract
The goal of this paper is to improve our previous Dynamic Obstacle Mapping (DOMap) system by means of improving the perception stage. The new system must deal with robots and people as dynamic obstacles using LIght Detection And Range (LIDAR) sensor in order [...] Read more.
The goal of this paper is to improve our previous Dynamic Obstacle Mapping (DOMap) system by means of improving the perception stage. The new system must deal with robots and people as dynamic obstacles using LIght Detection And Range (LIDAR) sensor in order to collect the surrounding information. Although robot movement can be easily tracked by an Extended Kalman Filter (EKF), people’s movement is more unpredictable and it might not be correctly linearized by an EKF. Therefore, to deal with a better estimation of both types of dynamic objects in the local map it is recommended to improve our previous work. The DOMap has been extended in three key points: first the LIDAR reflectivity remission is used to make more robust the matching in the optical flow of the detection stage, secondly static and a dynamic occlusion detectors have been proposed, and finally a tracking stage based on Particle Filter (PF) has been used to deal with robots and people as dynamic obstacles. Therefore, our new improved-DOMap (iDOMap) provides maps with information about occupancy and velocities of the surrounding dynamic obstacles (robots, people, etc.) in a more robust way and they are available to improve the following planning stage. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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23 pages, 4547 KiB  
Article
A Waypoint Tracking Controller for Autonomous Road Vehicles Using ROS Framework
by Rodrigo Gutiérrez, Elena López-Guillén, Luis M. Bergasa, Rafael Barea, Óscar Pérez, Carlos Gómez-Huélamo, Felipe Arango, Javier del Egido and Joaquín López-Fernández
Sensors 2020, 20(14), 4062; https://doi.org/10.3390/s20144062 - 21 Jul 2020
Cited by 18 | Viewed by 10461
Abstract
Automated Driving Systems (ADSs) require robust and scalable control systems in order to achieve a safe, efficient and comfortable driving experience. Most global planners for autonomous vehicles provide as output a sequence of waypoints to be followed. This paper proposes a modular and [...] Read more.
Automated Driving Systems (ADSs) require robust and scalable control systems in order to achieve a safe, efficient and comfortable driving experience. Most global planners for autonomous vehicles provide as output a sequence of waypoints to be followed. This paper proposes a modular and scalable waypoint tracking controller for Robot Operating System (ROS)-based autonomous guided vehicles. The proposed controller performs a smooth interpolation of the waypoints and uses optimal control techniques to ensure robust trajectory tracking even at high speeds in urban environments (up to 50 km/h). The delays in the localization system and actuators are compensated in the control loop to stabilize the system. Forward velocity is adapted to path characteristics using a velocity profiler. The controller has been implemented as an ROS package providing scalability and exportability to the system in order to be used with a wide variety of simulators and real vehicles. We show the results of this controller using the novel and hyper realistic CARLA Simulator and carrying out a comparison with other standard and state-of-art trajectory tracking controllers. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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