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Sensor and Communication Systems Enabling Autonomous Vehicles

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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 46425

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Special Issue Editors

Dr. Francisco J. Martinez

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Guest Editor

Department of Computer Science and System Engineering, University of Zaragoza, Zaragoza, Spain
Interests: VANET simulation; intelligent transportation systems; traffic safety; 802.11p; warning messages; Artificial Intelligence; vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Celimuge Wu

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Department of Computer and Network Engineering, Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan
Interests: ad hoc networks; sensor networks; intelligent transport systems; communication protocols; IoT; big data
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Dr. Syed Hassan Ahmed

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Guest Editor

Computer Science Department, California State University, Fullerton, CA 92831, USA
Interests: wireless communications; connected vehicles; named data; networks; information centric networks and ad hoc networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Johann M. Marquez-Barja

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Guest Editor

Faculty of Applied Engineering, Universiteit Antwerpen, 2000 Antwerpen, Belgium
Interests: 5G advanced heterogeneous dense cells architectures; elastic and flexible future wireless networks and its integration and impact on optical networks; IoT clustering; virtualization; provisioning and dynamic resource allocation towards dynamic converged networks; vehicular networks, mobility and handovering within smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Connected and autonomous vehicles (CAVs) have become a reality and represent a huge leap forward to improve our quality of life and traffic safety around the world. Driverless vehicles are a result of major advancements in both sensing and computation areas. By adding new communication capabilities, autonomous vehicles’ performance can even be improved. Further research efforts are required in the fields of vehicular networking, sensing, and autonomous driving, ranging from developments in sensors to computer vision or the use of 5G in self-driving vehicles.

This Special Issue focuses on the design, analysis, and implementation of smart sensing and communication issues, especially addressing autonomous vehicles. The objective is to provide an overview of the state of the art in the technological aspects of sensing, communications, computer vision, and artificial intelligence applied to CAVs.

More specifically, this Special Issue is seeking high-quality original contributions, soliciting high-level technical papers addressing the main research challenges related to the autonomous vehicles, sensing, and communications areas. Possible contributions should consist of original theoretical or practical analyses, never published elsewhere, and validated by simulations or real testbeds.

Potential topics include but are not limited to:

• Autonomous driving;
• Vehicular sensor networks;
• Sensor fusion in autonomous vehicles;
• 5G in vehicular networking;
• Multimedia communications in vehicular scenarios;
• Computer vision for autonomous vehicles;
• Content distribution in vehicular environments;
• Vehicular cloud computing and networking;
• Advanced services through vehicular communications;
• Security, trust, and privacy in vehicular communications;
• Models, simulators, and tools for intelligent transportation systems.

Prof. Francisco J. Martinez
Prof. Celimuge Wu
Prof. Syed Hassan Ahmed
Prof. Johann M. Marquez-Barja
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 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

Autonomous driving
Vehicular networks
Vehicle sensing
5G in the vehicular environment
Artificial Intelligence for autonomous driving
Computer vision for autonomous vehicles
Intelligent transportation systems

Published Papers (12 papers)

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Research

24 pages, 2727 KiB

Open AccessArticle

Communication Planning for Cooperative Terrain-Based Underwater Localization

by Jacob Anderson and Geoffrey A. Hollinger

Sensors 2021, 21(5), 1675; https://doi.org/10.3390/s21051675 - 01 Mar 2021

Cited by 6 | Viewed by 2054

Abstract

This paper presents a decentralized communication planning algorithm for cooperative terrain-based navigation (dec-TBN) with autonomous underwater vehicles. The proposed algorithm uses forward simulation to approximate the value of communicating at each time step. The simulations are used to build a directed acyclic graph that can be searched to provide a minimum cost communication schedule. Simulations and field trials are used to validate the algorithm. The simulations use a real-world bathymetry map from Lake Nighthorse, CO, and a sensor model derived from an Ocean Server Iver2 vehicle. The simulation results show that the algorithm finds a communication schedule that reduces communication bandwidth by 86% and improves robot localization by up to 27% compared to non-cooperative terrain-based navigation. Field trials were conducted in Foster Reservoir, OR, using two Riptide Autonomous Solutions micro-unmanned underwater vehicles. The vehicles collected GPS, altimeter, acoustic communications, and dead reckoning data while following paths on the surface of the reservoir. The data were used to evaluate the planning algorithm. In three of four missions, the planning algorithm improved dec-TBN localization while reducing acoustic communication bandwidth by 56%. In the fourth mission, dec-TBN performed better when using full communications bandwidth, but the communication policy for that mission maintained 86% of the localization accuracy while using 9% of the communications. These results indicate that the presented communication planning algorithm can maintain or improve dec-TBN accuracy while reducing the number of communications used for localization. Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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18 pages, 2363 KiB

Open AccessArticle

Deep Reinforcement Learning-Empowered Resource Allocation for Mobile Edge Computing in Cellular V2X Networks

by Dongji Li, Shaoyi Xu and Pengyu Li

Sensors 2021, 21(2), 372; https://doi.org/10.3390/s21020372 - 07 Jan 2021

Cited by 18 | Viewed by 3176

Abstract

With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, the dynamic environment of vehicles, and the long distances between the cloud servers and VUE induce some potential issues, such as extra communication delay and energy consumption. Fortunately, mobile edge computing (MEC), a promising computing paradigm, can ameliorate the above problems by enhancing the computing abilities of VUE through allocating the computational resources to VUE. In this paper, we propose a joint optimization algorithm based on a deep reinforcement learning algorithm named the double deep Q network (double DQN) to minimize the cost constituted of energy consumption, the latency of computation, and communication with the proper policy. The proposed algorithm is more suitable for dynamic scenarios and requires low-latency vehicular scenarios in the real world. Compared with other reinforcement learning algorithms, the algorithm we proposed algorithm improve the performance in terms of convergence, defined cost, and speed by around 30%, 15%, and 17%. Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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15 pages, 4450 KiB

Open AccessArticle

An Internet of Vehicles (IoV) Access Gateway Design Considering the Efficiency of the In-Vehicle Ethernet Backbone

by Dae-Young Kim, Minwoo Jung and Seokhoon Kim

Sensors 2021, 21(1), 98; https://doi.org/10.3390/s21010098 - 25 Dec 2020

Cited by 17 | Viewed by 3331

Abstract

A vehicular network is composed of an in-vehicle network (IVN) and Internet of Vehicles (IoV). IVN exchanges information among in-vehicle devices. IoV constructs Vehicle-to-X (V2X) networks outside vehicles and exchanges information among V2X elements. These days, in-vehicle devices that require high bandwidth is increased for autonomous driving services. Thus, the spread of data for vehicles is exploding. This kind of data is exchanged through IoV. Even if the Ethernet backbone of IVN carries a lot of data in the vehicle, the explosive increase in data from outside the vehicle can affect the backbone. That is, the transmission efficiency of the IVN backbone will be reduced due to excessive data traffic. In addition, when IVN data traffic is transmitted to IoV without considering IoV network conditions, the transmission efficiency of IoV is also reduced. Therefore, in this paper, we propose an IoV access gateway to controls the incoming data traffic to the IVN backbone and the outgoing data traffic to the IoV in the network environment where IVN and IoV are integrated. Computer simulations are used to evaluate the performance of the proposed system, and the proposed system shows better performance in the accumulated average transmission delay. Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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21 pages, 4629 KiB

Open AccessArticle

A Deep-Learning Model with Task-Specific Bounding Box Regressors and Conditional Back-Propagation for Moving Object Detection in ADAS Applications

by Guan-Ting Lin, Vinay Malligere Shivanna and Jiun-In Guo

Sensors 2020, 20(18), 5269; https://doi.org/10.3390/s20185269 - 15 Sep 2020

Cited by 7 | Viewed by 2779

Abstract

This paper proposes a deep-learning model with task-specific bounding box regressors (TSBBRs) and conditional back-propagation mechanisms for detection of objects in motion for advanced driver assistance system (ADAS) applications. The proposed model separates the object detection networks for objects of different sizes and applies the proposed algorithm to achieve better detection results for both larger and tinier objects. For larger objects, a neural network with a larger visual receptive field is used to acquire information from larger areas. For the detection of tinier objects, the network of a smaller receptive field utilizes fine grain features. A conditional back-propagation mechanism yields different types of TSBBRs to perform data-driven learning for the set criterion and learn the representation of different object sizes without degrading each other. The design of dual-path object bounding box regressors can simultaneously detect objects in various kinds of dissimilar scales and aspect ratios. Only a single inference of neural network is needed for each frame to support the detection of multiple types of object, such as bicycles, motorbikes, cars, buses, trucks, and pedestrians, and to locate their exact positions. The proposed model was developed and implemented on different NVIDIA devices such as 1080 Ti, DRIVE-PX2 and Jetson TX-2 with the respective processing performance of 67 frames per second (fps), 19.4 fps, and 8.9 fps for the video input of 448 × 448 resolution, respectively. The proposed model can detect objects as small as 13 × 13 pixels and achieves 86.54% accuracy on a publicly available Pascal Visual Object Class (VOC) car database and 82.4% mean average precision (mAP) on a large collection of common road real scenes database (iVS database). Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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20 pages, 1895 KiB

Open AccessArticle

Weather Classification Using an Automotive LIDAR Sensor Based on Detections on Asphalt and Atmosphere

by Jose Roberto Vargas Rivero, Thiemo Gerbich, Valentina Teiluf, Boris Buschardt and Jia Chen

Sensors 2020, 20(15), 4306; https://doi.org/10.3390/s20154306 - 01 Aug 2020

Cited by 22 | Viewed by 4610

Abstract

A semi-/autonomous driving car requires local weather information to identify if it is working inside its operational design domain and adapt itself accordingly. This information can be extracted from changes in the detections of a light detection and ranging (LIDAR) sensor. These changes are caused by modifications in the volumetric scattering of the atmosphere or surface reflection of objects in the field of view of the LIDAR. In order to evaluate the use of an automotive LIDAR as a weather sensor, a LIDAR is placed outdoor in a fixed position for a period of 9 months covering all seasons. As target, an asphalt region from a parking lot is chosen. The collected sensor raw data is labeled depending on the occurring weather conditions as: clear, rain, fog and snow, and the presence of sunlight: with or without background radiation. The influence of different weather types and background radiations on the measurement results is analyzed and different parameters are chosen in order to maximize the classification accuracy. The classification is done per frame in order to provide fast update rates while still keeping an F1 score higher than 80%. Additionally, the field of view is divided into two regions: atmosphere and street, where the influences of different weather types are most notable. The resulting classifiers can be used separately or together increasing the versatility of the system. A possible way of extending the method for a moving platform and alternatives to virtually simulate the scene are also discussed. Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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28 pages, 2270 KiB

Open AccessArticle

Network Service and Resource Orchestration: A Feature and Performance Analysis within the MEC-Enhanced Vehicular Network Context

by Nina Slamnik-Kriještorac, Erik de Britto e Silva, Esteban Municio, Henrique C. Carvalho de Resende, Seilendria A. Hadiwardoyo and Johann M. Marquez-Barja

Sensors 2020, 20(14), 3852; https://doi.org/10.3390/s20143852 - 10 Jul 2020

Cited by 12 | Viewed by 4139

Abstract

By providing storage and computational resources at the network edge, which enables hosting applications closer to the mobile users, Multi-Access Edge Computing (MEC) uses the mobile backhaul, and the network core more efficiently, thereby reducing the overall latency. Fostering the synergy between 5G and MEC brings ultra-reliable low-latency in data transmission, and paves the way towards numerous latency-sensitive automotive use cases, with the ultimate goal of enabling autonomous driving. Despite the benefits of significant latency reduction, bringing MEC platforms into 5G-based vehicular networks imposes severe challenges towards poorly scalable network management, as MEC platforms usually represent a highly heterogeneous environment. Therefore, there is a strong need to perform network management and orchestration in an automated way, which, being supported by Software Defined Networking (SDN) and Network Function Virtualization (NFV), will further decrease the latency. With recent advances in SDN, along with NFV, which aim to facilitate management automation for tackling delay issues in vehicular communications, we study the closed-loop life-cycle management of network services, and map such cycle to the Management and Orchestration (MANO) systems, such as ETSI NFV MANO. In this paper, we provide a comprehensive overview of existing MANO solutions, studying their most important features to enable network service and resource orchestration in MEC-enhanced vehicular networks. Finally, using a real testbed setup, we conduct and present an extensive performance analysis of Open Baton and Open Source MANO that are, due to their lightweight resource footprint, and compliance to ETSI standards, suitable solutions for resource and service management and orchestration within the network edge. Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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18 pages, 1461 KiB

Open AccessArticle

VNF Chain Placement for Large Scale IoT of Intelligent Transportation

by Xing Wu, Jing Duan, Mingyu Zhong, Peng Li and Jianjia Wang

Sensors 2020, 20(14), 3819; https://doi.org/10.3390/s20143819 - 08 Jul 2020

Cited by 4 | Viewed by 3102

Abstract

With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation. Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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21 pages, 21939 KiB

Open AccessArticle

V2X-Communication-Aided Autonomous Driving: System Design and Experimental Validation

by Chanyoung Jung, Daegyu Lee, Seungwook Lee and David Hyunchul Shim

Sensors 2020, 20(10), 2903; https://doi.org/10.3390/s20102903 - 20 May 2020

Cited by 29 | Viewed by 7286

Abstract

In recent years, research concerning autonomous driving has gained momentum to enhance road safety and traffic efficiency. Relevant concepts are being applied to the fields of perception, planning, and control of automated vehicles to leverage the advantages offered by the vehicle-to-everything (V2X) communication technology. This paper presents a V2X communication-aided autonomous driving system for vehicles. It is comprised of three subsystems: beyond line-of-sight (BLOS) perception, extended planning, and control. Specifically, the BLOS perception subsystem facilitates unlimited LOS environmental perception through data fusion between local perception using on-board sensors and communication perception via V2X. In the extended planning subsystem, various algorithms are presented regarding the route, velocity, and behavior planning to reflect real-time traffic information obtained utilizing V2X communication. To verify the results, the proposed system was integrated into a full-scale vehicle that participated in the 2019 Hyundai Autonomous Vehicle Competition held in K-city with the V2X infrastructure. Using the proposed system, the authors demonstrated successful completion of all assigned real-life-based missions, including emergency braking caused by a jaywalker, detouring around a construction site ahead, complying with traffic signals, collision avoidance, and yielding the ego-lane for an emergency vehicle. The findings of this study demonstrated the possibility of several potential applications of V2X communication with regard to autonomous driving systems. Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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19 pages, 790 KiB

Open AccessArticle

Application of Radio Environment Map Reconstruction Techniques to Platoon-based Cellular V2X Communications

by Sandra Roger, Carmen Botella, Juan J. Pérez-Solano and Joaquin Perez

Sensors 2020, 20(9), 2440; https://doi.org/10.3390/s20092440 - 25 Apr 2020

Cited by 9 | Viewed by 2874

Abstract

Vehicle platoons involve groups of vehicles travelling together at a constant inter-vehicle distance, with different common benefits such as increasing road efficiency and fuel saving. Vehicle platooning requires highly reliable wireless communications to keep the group structure and carry out coordinated maneuvers in a safe manner. Focusing on infrastructure-assisted cellular vehicle to anything (V2X) communications, the amount of control information to be exchanged between each platoon vehicle and the base station is a critical factor affecting the communication latency. This paper exploits the particular structure and characteristics of platooning to decrease the control information exchange necessary for the channel acquisition stage. More precisely, a scheme based on radio environment map (REM) reconstruction is proposed, where geo-localized received power values are available at only a subset of platoon vehicles, while large-scale channel parameters estimates for the rest of platoon members are provided through the application of spatial Ordinary Kriging (OK) interpolation. Distinctive features of the vehicle platooning use case are explored, such as the optimal patterns of vehicles within the platoon with available REM values for improving the quality of the reconstruction, the need for an accurate semivariogram modeling in OK, or the communication cost when establishing a centralized or a distributed architecture for achieving REM reconstruction. The evaluation results show that OK is able to reconstruct the REM in the platoon with acceptable mean squared estimation error, while reducing the control information for REM acquisition in up to 64% in the best-case scenario. Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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13 pages, 2189 KiB

Open AccessArticle

A Novel Approach for Mixed Manual/Connected Automated Freeway Traffic Management

by Duo Li and Peter Wagner

Sensors 2020, 20(6), 1757; https://doi.org/10.3390/s20061757 - 22 Mar 2020

Cited by 14 | Viewed by 2945

Abstract

Freeway traffic management and control often rely on input from fixed-point sensors. A sufficiently high sensor density is required to ensure data reliability and accuracy, which results in high installation and maintenance costs. Moreover, fixed-point sensors encounter difficulties to provide spatiotemporally and wide-ranging information due to the limited observable area. This research exploits the utilization of connected automated vehicles (CAVs) as an alternative data source for freeway traffic management. To handle inherent uncertainty associated with CAV data, we develop an interval type 2 fuzzy logic-based variable speed limit (VSL) system for mixed traffic. The simulation results demonstrate that when more 10% CAVs are deployed, the performance of the proposed CAV-based system can approach that of the detector-based system. It is demonstrated in addition that the introduction of CAVs may make VSL obsolete at very high CAV-equipment rates. Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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17 pages, 971 KiB

Open AccessArticle

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks

by Ran Duo, Celimuge Wu, Tsutomu Yoshinaga, Jiefang Zhang and Yusheng Ji

Sensors 2020, 20(4), 1082; https://doi.org/10.3390/s20041082 - 17 Feb 2020

Cited by 33 | Viewed by 4258

Abstract

With the arrival of 5G, the wireless network will be provided with abundant spectrum resources, massive data transmissions and low latency communications, which makes Vehicle-to-Everything applications possible. However, VANETs always accompany with frequent network topology changes due to the highly mobile feature of vehicles. As a result, the network performance will be affected by the frequent handover. In this paper, a seamless handover scheme is proposed where the Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are employed to adapt to the dynamic topology change in VANETs. The introduction of SDN provides a global view of network topology and centralized control, which enables a stable transmission layer connection when a handover takes place, so that the upper layer performance is not influenced by the network changes. By employing MEC server, the data are cached in advance before a handover happens, so that the vehicle can restore normal communication faster. In order to confirm the superiority of our proposal, computer simulations are conducted from different aspects. The results show that our proposal can significantly improve the network performance when a handover happens. Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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22 pages, 9089 KiB

Open AccessArticle

LoRa-Based Physical Layer Key Generation for Secure V2V/V2I Communications

by Biao Han, Sirui Peng, Celimuge Wu, Xiaoyan Wang and Baosheng Wang

Sensors 2020, 20(3), 682; https://doi.org/10.3390/s20030682 - 26 Jan 2020

Cited by 29 | Viewed by 3888

Abstract

In recent years, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication brings more and more attention from industry (e.g., Google and Uber) and government (e.g., United States Department of Transportation). These Vehicle-to-Everything (V2X) technologies are widely adopted in future autonomous vehicles. However, security issues have not been fully addressed in V2V and V2I systems, especially in key distribution and key management. The physical layer key generation, which exploits wireless channel reciprocity and randomness to generate secure keys, provides a feasible solution for secure V2V/V2I communication. It is lightweight, flexible, and dynamic. In this paper, the physical layer key generation is brought to the V2I and V2V scenarios. A LoRa-based physical key generation scheme is designed for securing V2V/V2I communications. The communication is based on Long Range (LoRa) protocol, which is able to measure Received Signal Strength Indicator (RSSI) in long-distance as consensus information to generate secure keys. The multi-bit quantization algorithm, with an improved Cascade key agreement protocol, generates secure binary bit keys. The proposed schemes improved the key generation rate, as well as to avoid information leakage during transmission. The proposed physical layer key generation scheme was implemented in a V2V/V2I network system prototype. The extensive experiments in V2I and V2V environments evaluate the efficiency of the proposed key generation scheme. The experiments in real outdoor environments have been conducted. Its key generation rate could exceed 10 bit/s on our V2V/V2I network system prototype and achieve 20 bit/s in some of our experiments. For binary key sequences, all of them pass the suite of statistical tests from National Institute of Standards and Technology (NIST). Full article

(This article belongs to the Special Issue Sensor and Communication Systems Enabling Autonomous Vehicles)

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