IoT-Based Vision Techniques in Autonomous Driving
AbstractAs more people drive vehicles, there is a corresponding increase in the number of deaths and injuries that happen due to road traffic accidents. Thus, various solutions have been proposed to reduce the impact of accidents. One of the most popular solutions is autonomous driving, which involves a series of embedded systems. These embedded systems assist drivers by providing crucial information on the traffic environment or by acting to protect the vehicle occupants in particular situations or to aid driving. Autonomous driving has the capacity to improve transportation services dramatically. Given the successful use of visual technologies and the implementation of driver assistance systems in recent decades, vehicles are prepared to eliminate accidents, congestion, collisions, and pollution. In addition, the IoT is a state-of-the-art invention that will usher in the new age of the Internet by allowing different physical objects to connect without the need for human interaction. The accuracy with which the vehicle's environment is detected from static images or videos, as well as the IoT connections and data management, is critical to the success of autonomous driving. The main aim of this review article is to encapsulate the latest advances in vision strategies and IoT technologies for autonomous driving by analysing numerous publications from well-known databases.
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Agarwal, P. K., Kumar, P., and Singh, H., 2020. Causes and Factors in Road Traffic Accidents at a Tertiary Care Center of Western Uttar Pradesh. In Medico Legal Update, 20(1): 38–4.
Ahmad, I., and Pothuganti, K., 2020. Design and Implementation of Real Time Autonomous Car by using Image Processing and IoT. 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 107–113.
Aravind, H., Sivraj, P., and Ramachandran, K. I., 2020. Design and Optimization of CNN for Lane Detection. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 1–6.
Banerjee, A., Chakraborty, C., Kumar, A., and Biswas, D., 2020. Emerging Trends in IoT and Big Data Analytics for Biomedical and Health Care Technologies. In Handbook of Data Science Approaches for Biomedical Engineering, 121–152.
Cao, J., Song, C., Peng, S., Xiao, F., and Song, S., 2019. Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles. in Sensors, 19(18): 4021–4042.
Celesti, A., Galletta, A., Carnevale, L., Fazio, M., Ĺay-Ekuakille, A., and Villari, M., 2018. An IoT Cloud System for Traffic Monitoring and Vehicular Accidents Prevention Based on Mobile Sensor Data Processing. in IEEE Sensors Journal, 18(12): 4795–4802.
Cheng, X., Zhang, R., and Yang, L., 2019. Wireless Toward the Era of Intelligent Vehicles. In IEEE Internet of Things Journal, 6(1): 188–202.
Chetouane, A., Mabrouk, S., Jemili, I., and Mosbah, M., 2020. Vision-based Vehicle Detection for Road Traffic Congestion Classification. Concurrency and Computation: Practice and Experience, e5983.
Choubey, P. C. S., and Verma, R., 2020. Vehicle Accident Detection, Prevention and Tracking System. International Research Journal of Engineering and Technology (IRJET), 7(8): 2658–2663.
Ciuntu, V., and Ferdowsi, H., 2020. Real-Time Traffic Sign Detection and Classification Using Machine Learning and Optical Character Recognition. In 2020 IEEE International Conference on Electro Information Technology (EIT), 480–486.
Da Xu, L., He, W., and Li, S., 2014. Internet of Things in Industries: A Survey, IEEE Transactions on Industrial Informatics, 10(4): 2233–2243.
Dhawale, R. Y., and Gavankar, N. L., 2019. Lane Detection and Lane Departure Warning System using Color Detection Sensor. 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, Kerala, India.
Eddie, E. E., Fabricio, D. E., Franklin, M. S., Paola, M. V., and Eddie D. G., 2018. Real-time Driver Drowsiness Detection based on Driver’s Face Image behavior using a System of Human Computer Interaction Implemented in a Smartphone. In International Conference on Information Technology & Systems. Springer, Cham.
Elbasani, E., Siriporn, P., and Choi, J. S., 2020. A Survey on RFID in Industry 4.0. In Internet of Things for Industry 4.0, Springer, Cham, 1–16.
Elbery, A., Hassanein, H. S., Zorba, N., and Rakha, H. A., 2020. IoT-Based Crowd Management Framework for Departure Control and Navigation. In IEEE Transactions on Vehicular Technology.
Fernando, W. U. K., Samarakkody, R. M., and Halgamuge, M. N., 2020. Smart Transportation Tracking Systems based on the Internet of Things Vision. In Connected Vehicles in the Internet of Things, Springer, Cham, 143–166.
Garg, T., Kagalwalla, N., Churi, P., Pawar, A., and Deshmukh, S., 2020. A Survey on Security and Privacy Issues in IoV. International Journal of Electrical & Computer Engineering, 10(5): 5409, 5419.
Hamid, K., Bahman, J., Mahdi, J. M., and Reza, N. J., 2020. Artificial Intelligence and Internet of Things for Autonomous Vehicles. In Nonlinear Approaches in Engineering Applications, Springer, Cham, 39–68.
Hanan, E., 2019. Internet of Things (IoT), Mobile Cloud, Cloudlet, Mobile IoT, IoT Cloud, Fog, Mobile Edge, and Edge Emerging Computing Paradigms: Disambiguation and Research Directions. Journal of Network and Computer Applications, 128: 105–140.
Hirz, M., and Walzel, B., 2018. Sensor and Object Recognition Technologies for Self-Driving Cars. in Computer-Aided Design and Applications, 15(4): 501–508.
Hsu, S., Huang, C. L., and Cheng, C. H., 2018. Vehicle Detection using Simplified Fast R-CNN. In 2018 International Workshop on Advanced Image Technology (IWAIT), IEEE, 1–3.
Izquierdo-Reyes, J., Ramirez-Mendoza, R. A., Bustamante-Bello, M. R., Navarro-Tuch, S., and Avila-Vazquez, R., 2018. Advanced Driver Monitoring for Assistance System (ADMAS). International Journal on Interactive Design and Manufacturing (IJIDeM), 12(1): 187–197.
Kang, J., Yu, R., Huang, X., and Zhang, Y., 2018. Privacy-Preserved Pseudonym Scheme for Fog Computing Supported Internet of Vehicles. In IEEE Transactions on Intelligent Transportation Systems, 19(8): 2627–2637.
Khalifa, A. B., Alouani, I., Mahjoub, M. A., and Amara, N. E. B., 2020. Pedestrian Detection Using a Moving Camera: A Novel Framework for Foreground Detection. In Cognitive Systems Research, 60: 77–96.
Kim, B., Yuvaraj, N., Sri, P., Santhosh, K. R. R., and Sabari, A., 2020. Enhanced Pedestrian Detection using Optimized Deep Convolution Neural Network for Smart Building Surveillance. Soft Computing, 24: 17081–17092.
Kocić, J., Jovičić, N., and Drndarević, V., 2018. Sensors and Sensor Fusion in Autonomous Vehicles. 2018 26th Telecommunications Forum (TELFOR), Belgrade, 420–425.
Koresh, M. H. J. D., and Deva, J., 2019. Computer Vision Based Traffic Sign Sensing for Smart Transport. Journal of Innovative Image Processing (JIIP), 1(1): 11–19.
Kumar, A., and Patra, R., 2018. Driver drowsiness monitoring system using visual behaviour and machine learning. 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, 339–344.
Lex, F., Daniel, E. B., Michael, G., William, A., Spencer, D., Bendikt, J., Jack, T., Aleksandr, P., Julia, K., Li, D., Sean, S., Alea, M., Andrew, S., Anthony, P., Bobbie, D. S., Linda, A., Bruce, M., and Bryan, R., 2019. MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction with Automation. In IEEE Access, 7: 102021–102038.
Li, G., Wang, Z., Fei, X., Li, J., Zheng, Y., Li, B., and Zhang, T., 2021. Identification and Elimination of Cancer Cells by Folate-Conjugated CdTe/CdS Quantum Dots Chiral Nano-Sensors. Biochemical and Biophysical Research Communications, 560: 199–204.
Li, W. L., Li, X. G., Qin, Y. Y., Ma, D., Cui, W., and Province, J., 2020. Real-time Traffic Sign Detection Algorithm Based on Dynamic Threshold Segmentation and SVM. Journal of Computers, 31(6): 258–273.
Litman, T., 2020. Autonomous Vehicle Implementation Predictions: Implications for Transport Planning.
Liu, J., Lou, L., Huang, D., Zheng, Y., and Xia, W., 2018. Lane Detection based on Straight Line Model and K-means Clustering. In 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), IEEE, 527–532.
Liu, Z., Peng, Y., and Hu, W., 2020. Driver Fatigue Detection based on Deeply-Learned Facial Expression Representation. In Journal of Visual Communication and Image Representation, 71: 102723.
Lu, H., Liu, Q., Tian, D., Li, Y., Kim, H., and Serikawa, S., 2019. The Cognitive Internet of Vehicles for Autonomous Driving. In IEEE Network, 33(3): 65–73.
Minhas, A. A., Jabbar, S., Farhan, M., and ul Islam, M. N., 2019. Smart Methodology for Safe Life on Roads with Active Drivers based on Real-Time Risk and Behavioural Monitoring. Journal of Ambient Intelligence and Humanized Computing, Springer, 1–13.
Minovski, D., Åhlund, C., and Mitra, K., 2020. Modeling Quality of IoT Experience in Autonomous Vehicles. IEEE Internet of Things Journal, 7(5): 3833–3849.
Narote, S. P., Bhujbal, P. N., Narote, A. S., and Dhane, D. M., 2018. A Review of Recent Advances in Lane Detection and Departure Warning System. In Pattern Recognition, 73: 216–234.
Ng, J. R., Wong, J. S., Goh, V. T., Yap, W. J., Yap, T. T. V., and Ng, H., 2019. Identification of Road Surface Conditions using IoT Sensors and Machine Learning. Computational Science and Technology, Springer, Singapore, 481.
Olanrewaju, R. F., Fakhri, A. S. A., Sanni, M. L., and Ajala, M. T., 2019. Robust, Fast and Accurate Lane Departure Warning System using Deep Learning and Mobilenets. In 2019 7th International Conference on Mechatronics Engineering (ICOM), IEEE, 1–6.
Qu, H., Yuan, T., Sheng, Z., and Zhang, Y., 2018. A Pedestrian Detection Method based on YOLOv3 Model and Image Enhanced by Retinex. 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE.
Philip, J. M., Durga, S., and Esther, D., 2021. Deep Learning Application in IoT Health Care: A survey. In Intelligence in Big Data Technologies-Beyond the Hype, Springer, Singapore, 199–208.
Pizzati, F., Allodi, M., Barrera, A., and García, F., 2019. Lane Detection and Classification using Cascaded CNNs. In International Conference on Computer Aided Systems Theory, Springer, Cham, 95–103.
Pranav, K. B., and Manikandan, J., 2020. Design and Evaluation of a Real-time Pedestrian Detection System for Autonomous Vehicles. In 2020 Zooming Innovation in Consumer Technologies Conference (ZINC), IEEE, 155–159.
Preethaa, K. S., and A. Sabari, A., 2020. Intelligent Video Analysis for Enhanced Pedestrian Detection by Hybrid Metaheuristic Approach. Soft Computing, 1–9.
Rahul, P. K., Abhishek, S. K., Zeeshaan, W. S., Vaibhav, U. B., and Nasiruddin, M., 2020. IoT based Self Driving Car. International Research Journal of Engineering and Technology (IRJET), 7(3): 5177–5181.
Rani, R., Kumar, N., Khurana, M., Kumar, A., and Barnawi, A., 2021. Storage as a Service in Fog Computing: A Systematic Review. Journal of Systems Architecture, 102033.
Ravikumar, S., and Kavitha, D., 2021. IOT based Autonomous Car Driver Scheme based on ANFIS and Black Widow Optimization. Journal of Ambient Intelligence and Humanized Computing, 1–14.
Sadiku, M. N., Tembely, M., and Musa, S. M., 2018. Internet of Vehicles: An Introduction. International Journal of Advanced Research in Computer Science and Software Engineering, 8(1): 11.
Sakhare, K. V., Tewari, T., and Vyas, V., 2020. Review of Vehicle Detection Systems in Advanced Driver Assistant Systems. Archives of Computational Methods in Engineering, 591–610.
Sastry, A., Bhargav, K., Pavan, K. S., and Narendra, M., 2020. Smart Street Light System using IoT. International Research Journal of Engineering and Technology (IRJET), 7(3): 3093–3097.
Savaş, B. K., and Becerikli, Y., 2020. Real Time Driver Fatigue Detection System based on Multi-Task ConNN. In IEEE Access, 8: 12491–12498.
Sharma, S., 2021. Towards Artificial Intelligence Assisted Software Defined Networking for Internet of Vehicles. In Intelligent Technologies for Internet of Vehicles. Springer, Cham, 191–222.
Shashank, P., Pavan, M., Niharika, P., Jalakam, S., and Sathweek, B., 2020. Machine Learning Based Water Quality Checker and pH Verifying Model. European Journal of Molecular & Clinical Medicine, 7(11): 2220–2228.
Singh, D., Tripathi, G., and Jara, A.J., 2014. A Survey of Internet-of-Things: Future Vision, Architecture, Challenges and Services, 2014 IEEE World Forum on Internet of Things (WF-IoT). IEEE.
Srivastava, M., and Kumar, R., 2021. Smart Environmental Monitoring Based on IoT: Architecture, Issues, and Challenges. In Advances in Computational Intelligence and Communication Technology, Springer, 349–358.
Sultana, T., and Wahid, K. A., 2019. IoT-Guard: Event-Driven Fog-Based Video Surveillance System for Real-Time Security Management. In IEEE Access, 7: 134881–134894.
Tsai, C., Ching-Kan, T., Ho-Chia, T., and Jiun-In, G., 2018. Vehicle Detection and Classification based on Deep Neural Network for Intelligent Transportation Applications. In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE, 1605–1608.
Uma, S., and Eswari, R., 2021. Accident Prevention and Safety Assistance using IOT and Machine Learning. Journal of Reliable Intelligent Environments, 1–25.
Varma, B., Sam, S., and Shine, L., 2019. Vision Based Advanced Driver Assistance System Using Deep Learning. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 1–5.
Wang, J., Lim, M. K., Wang, C., and Tseng, M. L., 2021. The evolution of the Internet of Things (IoT) over the past 20 years. Computers & Industrial Engineering, 155: 107174.
Wei, Y., Tian, Q., Guo, J., Huang, W., and Cao, J., 2019. Multi-Vehicle Detection Algorithm through Combining Harr and HOG Features. Mathematics and Computers in Simulation, 155: 130–145.
Whitmore, A., Agarwal, A., and Da Xu, L., 2015. The Internet of Things-A Survey of Topics and Trends, Information Systems Frontiers, 17(2): 261–274.
World Health Organization (WHO)., 2020. Global Status Report on Road Safety. Accessed on: 28th October 2021. [Online]. Available: https://www.who.int/violence_injury_prevention/road_safety_status/report/en/.
Xiang, X., Zhai, M., Lv, N., and El Saddik, A., 2018. Vehicle Counting based on Vehicle Detection and Tracking from Aerial Videos. In Sensors, 18(8): 2560.
Zahid, A., Iniyavan, R., and Madhan, M. P., 2019. Enhanced vulnerable pedestrian detection using deep learning. International Conference on Communication and Signal Processing (ICCSP), IEEE, 0971–0974.
Zaidan, A. A., and Zaidan, B. B., 2020. A Review on Intelligent Process for Smart Home Applications based on IoT: Coherent Taxonomy, Motivation, Open Challenges, and Recommendations. Artificial Intelligence Review, 53(1): 141–165.
Zang, J., Zhou, W., Zhang, G., and Duan, Z., 2018. Traffic Lane Detection using Fully Convolutional Neural Network. In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE, 305–311.
Zhang, J., Xie, Z., Sun, J., Zou, X., and Wang, J., 2020. A Cascaded R-CNN with Multiscale Attention and Imbalanced Samples for Traffic Sign Detection. In IEEE Access, 8: 29742–29754.
Zhao, C., Li, L., Xin, P., Zhiheng, L., Fei-Yue, W., and Xiangbin, W., 2021. A Comparative Study of State-of-the-art Driving Strategies for Autonomous Vehicles. Accident Analysis & Prevention. 150: 105937.