Road signs classification by ANN for real-time implementation

Hamdi, Sabrine; Faiedh, Hassène; Souani, Chokri; Besbes, Kamel

doi:10.1109/cadiag.2017.8075679

Cited by 14 publications

(20 citation statements)

References 26 publications

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“…In [261], the authors used ANN for real-time traffic sign classification and identification. They classified signs into different shapes (triangle, square, etc.)…”

Section: ) Road Sign and Traffic Signal Identificationmentioning

confidence: 99%

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

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Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and lowlatency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driverless vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed. INDEX TERMS Machine learning, VANET, cognitive radio, autonomous vehicles, smart transportation system. performance of CR-VANET [15]. Security enhancement is one of the major issues in CR-VANET. Here, a vehicle can pretend to be a PU and propagate false information to obtain spectrum access selfishly. ML can be used to detect such actions and enhance security [16], [17]. ML also provides an optimum route to CR-VANET users to avoid traffic jams and road accidents. ML can also play a vital role in the best infotainment experience in CR-VANET. It can be used for appropriate scheduling, selecting the best channel, and prioritizing messages. CR and ML can play a major role in the next-generation driverless car system. The role of CR in the next-generation transportation system has been presented in previous discussions. This survey shows how ML can be applied to reduce road accidents and traffic congestion. CR can be used to accommodate the spectrum required to support massive data communication among automated driverless vehicles and networks. ML can be an integral part of this driverless or automated vehicle system. Similar to a robot, an autonomous vehicle (AV) can learn the surrounding environment and communicate with increased safety, reliability, QoS, and energy efficiency by applying such learning. This paper presents the dynamic usages of ML in CR-VANET elaborately. Several of the benefits of CR in VANETs and ML in VANETs and CR-VANETs are presented in Table 1. B. CONTRIBUTIONS OF THIS SURVEY ARTICLE Many survey articles describe CR, VANET, ML, and CR-VANETs individually or describe a few aspects of their amalgamation. To the best of our knowledge, surveys that cover the usage of ML in CR-VANET...

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“…In [261], the authors used ANN for real-time traffic sign classification and identification. They classified signs into different shapes (triangle, square, etc.)…”

Section: ) Road Sign and Traffic Signal Identificationmentioning

confidence: 99%

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

View full text Add to dashboard Cite

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“…Considering some specificities of autonomous truck and its risks, at least a few more studies about the topic could be expected. [64], [67], [70], [61], [65], [69], [62], [58], [63], [71], [72], [74], [68], [ [39], [18], [32], [31], [33], [26], [28], [30], [55], [52], [29], [ [75], [41], [29], [20], [44], [35], Prediction of adc vanced driver assistance systems (ADAS) remaining useful life (RUL) for the prognosis of ADAS safety critical components Pedestrian Detection; How to "automate" manual annotation for images to train visual perception for AVs Road junction detection; [52], [27], [37], [30] Bayesian Artificial Intelligence…”

Section: Final Remarksmentioning

confidence: 99%

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Nascimento

Vismari

Molina

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and techniques. However, while some researchers in this field believe AI is the core element to enhance safety, others believe AI imposes new challenges to assure the safety of these new AI-based systems and applications. In this non-convergent context, this paper presents a systematic literature review to paint a clear picture of the state of the art of the literature in AI on AV safety. Based on an initial sample of 4870 retrieved papers, 59 studies were selected as the result of the selection criteria detailed in the paper. The shortlisted studies were then mapped into six categories to answer the proposed research questions. An AV system model was proposed and applied to orient the discussions about the SLR findings. As a main result, we have reinforced our preliminary observation about the necessity of considering a serious safety agenda for the future studies on AIbased AV systems.

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“…Shi et al have proposed Real-Time Traffic Light Detection with Adaptive Background Suppression Filter [10]. Hamdi et al have proposed to system for Road signs Classification by ANN for Real-Time Implementation [3]. This system proposes a real-time algorithm for shape classification of traffic signs and their recognition to provide a driver alert system.…”

Section: Behrendt Et Al Have Proposed a Deep Learning Approach Tomentioning

confidence: 99%

“…The systems presented in [1], [4], [7], [8], [10], [11] and [12] fail in considering a vast variety of both Training and Testing datasets, that can be considered to scale the respective systems. [3] and [6] fail on being tested in harsh weather conditions. [2] requires high quality hardware components, to achieve the expected levels of accuracy.…”

Section: Behrendt Et Al Have Proposed a Deep Learning Approach Tomentioning

confidence: 99%

Real Time Traffic Light Detection by Autonomous Vehicles using Artificial Neural Network Techniques

Mahesh¹,

T²

2019

IJITEE

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Autonomous vehicles are the reality of the future, they will open up the way for future advanced systems where computers are expected to take over the decision making of driving. These automobiles are capable of sensing their environment and moving with little or no human input. The main goal of this research is to detect traffic light in real-time for autonomous vehicles. Apart from taking decisions to navigate in the right manner the autonomous vehicles important task is to detect traffic lights, so that it can obey the traffic rules with sufficient precision. The work carried out in this research makes use of two Artificial Intelligence technique, these techniques are compared in accomplishing the task of traffic light detection in real time. The two models that are designed and implemented are Convolution neural network (CNN) and Deep Convolution Inverse Graphics Network (DCIGN). The results clearly show that DCIGN out performance CNN by 8%.

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Road signs classification by ANN for real-time implementation

Cited by 14 publications

References 26 publications

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Real Time Traffic Light Detection by Autonomous Vehicles using Artificial Neural Network Techniques

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