A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

Ahangar, M. Nadeem; Ahmed, Qasim Zeeshan; Khan, Fahd Ahmed; Hafeez, Maryam

doi:10.3390/s21030706

Cited by 220 publications

(111 citation statements)

References 190 publications

(272 reference statements)

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“…Object detection and tracking are challenging tasks and play an essential role in many visual-based applications. At present, the deep learning field provides several methods for advancing the automation levels by improving the environment perception [3]. The autonomous vehicles have developed from Level-0 class with no automation to Level-1 with driver assistance automation.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Haris

Głowacz

2021

Electronics

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Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-time deployment restrictions. We conclude that the YOLOv4 outperforms accurately in detecting difficult road target objects under complex road scenarios and weather conditions in an identical testing environment.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Haris

Głowacz

2021

Electronics

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“…Due to the high complexity and the wide range of functionalities of the modern vehicular infotainment systems, a practical application based on developing a system capable of estimating the driving complexity in real time is presented [3]. In order to fulfil the vehicular networks environment, an international American standard for V2V and V2I communications has been proposed by the IEEE 802.11 working group for short-range inter-vehicle communication in the 5.9 GHz frequency band called IEEE 802.11p [4,5]. IEEE 802.11 p allows vehicles to directly communicate with each other without the need to go through the infrastructure.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Approach for Autonomous Vehicle Based on Optimization Technique for Enhancing the Communication between Autonomous Vehicle-to-Everything through Cooperative Communication

Osman

Abdelsalam

2021

Applied Sciences

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Recent autonomous intelligent transportation systems commonly adopt vehicular communication. Efficient communication between autonomous vehicles-to-everything (AV2X) is mandatory to ensure road safety by decreasing traffic jamming, approaching emergency vehicle warning, and assisting in low visibility traffic. In this paper, a new adaptive AV2X model, based on a novel optimization method to enhance the connectivity of the vehicular networks, is proposed. The presented model optimizes the inter-vehicle position to communicate with the autonomous vehicle (AV) or to relay information to everything. Based on the system quality-of-service (QoS) being achieved, a decision will be taken whether the transmitting AV communicates directly to the destination or through cooperative communication. To achieve the given objectives, the best position of the relay-vehicle issue was mathematically formulated as a constrained optimization problem to enhance the communication between AV2X under different environmental conditions. To illustrate the effectiveness of the proposed model, the following factors are considered: distribution of vehicles, vehicle density, vehicle mobility and speed. Simulation results show how the proposed model outperforms other previous models and enhances system performance in terms of four benchmark aspects: throughput (S), packet loss rate (PLR), packet delivery ratio (PDR) and average delivery latency (DL).

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“…However, its limited penetration though solid objects makes it unreliable and inaccurate for indoor positioning [ 17 ]. To address this, several techniques including Wireless Fidelity (WiFi), Radio Frequency Identification(RFID), Bluetooth Low Energy (BLE) and ZigBee, where the localization is performed based on the received signal strength have been explored [ 18 , 19 , 20 , 21 , 22 ]. However, these techniques have drawbacks such as low accuracy, low data-rate and/or short range communication [ 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…To address this, several techniques including Wireless Fidelity (WiFi), Radio Frequency Identification(RFID), Bluetooth Low Energy (BLE) and ZigBee, where the localization is performed based on the received signal strength have been explored [ 18 , 19 , 20 , 21 , 22 ]. However, these techniques have drawbacks such as low accuracy, low data-rate and/or short range communication [ 18 , 19 , 20 , 21 , 22 ]. Furthermore, when using narrow-band device-free indoor communication, multipath fading and a cluttered environment make it difficult to design a low-cost and low-complexity receiver [ 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems

Abbas

Che

Ahmed

et al. 2021

Sensors

Self Cite

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In this paper, an analytical framework is presented for device detection in an impulse radio (IR) ultra-wide bandwidth (UWB) system and its performance analysis is carried out. The Neyman–Pearson (NP) criteria is employed for this device-free detection. Different from the frequency-based approaches, the proposed detection method utilizes time domain concepts. The characteristic function (CF) is utilized to measure the moments of the presence and absence of the device. Furthermore, this method is easily extendable to existing device-free and device-based techniques. This method can also be applied to different pulse-based UWB systems which use different modulation schemes compared to IR-UWB. In addition, the proposed method does not require training to measure or calibrate the system operating parameters. From the simulation results, it is observed that an optimal threshold can be chosen to improve the ROC for UWB system. It is shown that the probability of false alarm, PFA, has an inverse relationship with the detection threshold and frame length. Particularly, to maintain PFA<10−5 for a frame length of 300 ns, it is required that the threshold should be greater than 2.2. It is also shown that for a fix PFA, the probability of detection PD increases with an increase in interference-to-noise ratio (INR). Furthermore, PD approaches 1 for INR >−2 dB even for a very low PFA i.e., PFA=1×10−7. It is also shown that a 2 times increase in the interference energy results in a 3 dB improvement in INR for a fixed PFA=0.1 and PD=0.5. Finally, the derived performance expressions are corroborated through simulation.

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A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

Cited by 220 publications

References 190 publications

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

A Novel Adaptive Approach for Autonomous Vehicle Based on Optimization Technique for Enhancing the Communication between Autonomous Vehicle-to-Everything through Cooperative Communication

Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems

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