Smart Highway Road Side Symbol Detection

Abstract

Automatic Discovery of road signs has lately entered attention from the computer vision exploration community.Themainidealof thissystem isto descry signs of a moving vehicle. Road Traffic subscribe Discovery is a technologybywhichavehicleissuitabletofetethebusiness signs put on the road e.g.\" speed limit\" or\" children\" or \" turn ahead\". Consider a condition stoner is driving a auto at night or in stormy season also it is n\'t possible for motorists to keep watch on each and every road symbol or the communication plates like turn, speed swell, academy, diversionetc.Thisisveritablyusefuldesigninthiscondition Then we will use one signal transmitter on each and every symbolorcommunicationplateonroadsideandwhenever any vehicle passes from that symbol the transmitter positioned inside the vehicle will admit the signals and displaypropercommunicationorthesymboldetailsonTV connectedtoauto.Nowmotoristcanconcentrateondriving. We\'re trying to apply the system with signal identification using the radio frequency technology. The business signal suggestion will inform the motorist of the current signal statusinsidetheautoonthedashboard.TheRFtransmitter willtransmittheRFsignalandthesetransmittedsignalwill enteredbyRFReceiverfittedtothevehicleandwillinform motoristaboutthecurrentsignalstatusofthebusinesslight to perform necessary action.

Introduction

The text discusses recent advancements and research in autonomous vehicles (AVs) and intelligent transportation systems, focusing on improving vehicle perception, communication, and safety.

1. Autonomous Vehicles and AI: AVs use advanced AI, particularly Convolutional Neural Networks (CNNs), for recognizing road signs and navigating with minimal human input. CNN-based classifiers achieve very high accuracy (~99.8%), enabling real-time road sign detection.

2. Object Detection in Challenging Conditions: To enhance object detection under adverse weather (fog, rain), ensemble deep learning models combined with data augmentation improve detection accuracy and performance on resource-limited devices.

3. Perception and Decision-Making: Perception modules using images and LiDAR data are critical for AV navigation. The paper surveys state-of-the-art semantic segmentation and object detection techniques, sensor types, datasets, and simulation tools.

4. Accident Alert Systems: An Accident Alert Light and Sound (AALS) system is proposed to detect accidents on smart roads and warn nearby vehicles in real time without requiring vehicle modifications, aiming to reduce multi-vehicle collisions and fatalities.

5. Traffic Sign Detection Improvements: Techniques like YOLOv3 with pruning and patch-wise training address challenges in detecting small traffic signs, improving recall and precision.

6. Speed Sensor Detection: Systems employing IR sensors and microcontrollers detect speeding vehicles, alerting authorities via smartphones to enhance law enforcement and road safety.

7. Automatic Speed Control: RFID technology is used for automatic speed regulation in speed-limited zones (schools, hospitals) by interacting with vehicle-mounted RFID readers to reduce speeding without driver intervention.

8. Lightweight Neural Networks: Lightweight CNN architectures are developed for traffic sign recognition to balance accuracy and computational efficiency, making them suitable for portable devices with high accuracy (>99%).

9. Traffic Sign Recognition Using ML: Machine learning methods like LSTM and stacking meta-learners improve traffic sign detection reliability and reduce misclassification.

10. Road-Type Detection: Systems leveraging onboard video and sensor data identify road types without GPS, achieving good precision for European and UK roads.

11. Vehicle-to-Infrastructure Communication: Various RFID-based systems and VANETs are discussed for real-time traffic monitoring, vehicle positioning, and warning message transmission, highlighting RFID’s advantages over GPS/GPRS for local communication and accuracy.

12. Proposed System: A system with three modules is proposed:

• RF transmitters on traffic signs broadcast unique IDs.

• RF receivers in vehicles capture these signals.

• A microcontroller processes and displays relevant traffic symbol information to drivers.

This system aims to improve driver awareness of traffic signals and road conditions through reliable vehicle-infrastructure communication.

Conclusion

Asthesystemismainlydesignedfordriverassistanceontheway orincityitgiveswiderangeofscopetotheuseror implementer. 1) Helpful in Night traveling: Much time it is not possible for drivertokeepwatchoneachsignalandboardonthehighways so the system will help drivers to understand the symbol or signals. 2) Noneedtoseeboardonroad: Assystemiscapableofshow the symbol details user can concentrate on driving only. 3) Distance tower can show all spots: If user misses the signal or can’t see the signal or warning from long distance system can able to see the details of board from long distance. 4) Viewing signal information: It may possible that user can’t read long messages on board so the system will also help the driver. The Road Side Symbol Detection System offers a reliable and efficient method of assisting drivers by providing real- time traffic sign information using RF technology. Unlike traditionalimage-basedsystems,RFcommunicationensures accuracy even in adverse conditions such as nighttime drivingorbadweather.Thesystemeffectivelyreducesdriverdistractionsbyeliminatingtheneedtovisuallylocateroad signs. Future advancements could integrate AI-based predictive alerts, extended RF range, and V2X (Vehicle-to-Everything) communicationforenhancedroadsafety.Thistechnologyhas the potential to be adopted in modern smart transportation systems, significantly improving driving efficiency and road safety.

References

[1] Ayachi,R.,Said,Y.E.,&Atri,M.(2019).Toperformroad signsrecognitionforautonomousvehiclesusingcascaded deeplearningpipeline. ArtificialIntelligenceAdvances, 1(1), 1-10. [2] Walambe, R., Marathe, A., Kotecha, K., &Ghinea, G. (2021).Lightweightobjectdetectionensembleframework for autonomous vehicles in challenging weather conditions. Computational Intelligence and Neuroscience, 2021(1), 5278820. [3] Jebamikyous, H. H., &Kashef, R. (2022). Autonomous vehicles perception (avp) using deep learning: Modeling, assessment, and challenges.IEEE Access,10, 10523- 10535. [4] Mateen,A.,Hanif,M.Z.,Khatri,N.,Lee,S.,&Nam,S.Y.(2022).Smartroadsforautonomousaccidentdetection and warnings. Sensors, 22(6), 2077. [5] Rehman,Y.,Amanullah,H.,Shirazi,M.A.,&Kim,M.Y. (2022). Small traffic sign detection in big images: Searchingneedleinahay.IEEEAccess,10,18667-18680. [6] Azidin, F. A., Zamri, M. S. A., &Zainudin, Z. (2023). EnhancingRoadSafety:ASmartSpeedDetectionSystem Using IoT Technology.Journal of Telecommunication, Electronic and Computer Engineering (JTEC),15(4), 27- 32. [7] Dhote,D.R.,Lakde,P.R.,Pachare,A.S.,Meharkure,M. A., &Dongare, M. (2021). Automatic Speed Control of VehiclebyusingRFIDTechnology.InternationalJournal ofResearchinEngineering,Scienceand Management, 4(7), 268-270. [8] Mamatkulovich,B.B.(2022).Lightweightresiduallayers based convolutional neural networks for traffic sign recognition. European International Journal of MultidisciplinaryResearchandManagementStudies, 2(05), 88-94. [9] Fazekas,Z.,Balázs,G.,Gyulai,C.,Potyondi,P.,&Gáspár,P.(2022).Road?TypeDetectionBasedonTrafficSignand Lane Data.Journal of advanced transportation,2022(1), 6766455. [10] Atif, M., Zoppi, T., Gharib, M., &Bondavalli, A. (2022). Towardsenhancingtrafficsignrecognitionthroughsliding windows. Sensors, 22(7), 2683. [11] Murthy,J.S.,Siddesh,G.M.,Lai,W.C.,Parameshachari,B. D., Patil, S. N., &Hemalatha, K. L. (2022). ObjectDetect: A Real?Time Object Detection Framework forAdvancedDriverAssistantSystemsUsing YOLOv5. Wireless Communications and MobileComputing,2022(1),9444360. [12] Khan, M. A., Park, H., &Chae, J. (2023). A lightweight convolutional neural network (CNN) architecture for traffic sign recognition in urban road networks. Electronics,12(8), 1802. [13] Dhawan,K.,&RK,N.(2023).Identificationoftrafficsigns foradvanceddrivingassistancesystemsinsmartcitiesusing deep learning.Multimedia tools and applications,82(17), 26465-26480. [14] Nadeem,H.,Javed,K.,Nadeem,Z.,Khan,M.J.,Rubab,S., Yon, D. K., &Naqvi, R. A. (2023). Road feature detection foradvancedriverassistancesystemusingdeeplearning. Sensors, 23(9), 4466. [15] Padaria,A.A.,Mehta,A.A.,Jadav,N.K.,Tanwar,S.,Garg,D., Singh, A., ...& Sharma, G. (2023). Traffic sign classification for autonomous vehicles using split and federated learning underlying 5g.IEEE Open Journal of Vehicular Technology, 4, 877-892. [16] C. Manchanda, R. Rathi, and N. Sharma, \"Traffic density investigation & road accident analysis in India using deep learning,\" in 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2019: IEEE, pp. 501-506. [17] A. Salim, M. Ali, and E. H. Al-Hemairy, \"Communication ModuleforV2XApplicationsusingEmbeddedSystems,\"inJournalofPhysics: Conference Series, 2021,vol. 1933, no. 1: IOP Publishing, p. 012112. [18] M. Altuijary, \"An Overview of Vehicle-to-Infrastructure Communication Technology,\"DegreeofMaster, Facultyof The Graduate College, University of Nebraska 2020. [19] [20] W. A. Abdelkarim, \"Vehicle to Infrastructure Communication for Adaptive Traffic Management,\" Sudan University of Science and Technology, 2016. [21] O. Javaid, \"Autonomous Vehicle Navigation and CommunicationbyRadioFrequencyIdentityTags,\"Master thesis, Electrical and Computer, Carleton University, Carleton University Ottawa, Ontario, 2021. [22] J. F. SchneckerSr, B. Byrtus, J. Yang-Tsui, B. Jaime, E. Leary,andK.Luo,\"Satellite-basedPositioning,Navigation, and Timing (PNT),\" Naval Information Warfare Center Pacific, 2022. [23] S. Dahal, J. Hernandez, and J. Roesler, \"Infrastructure Enhancements for CAV Navigation,\" in \"3,\" University of Michigan. Center for Connected and Automated Transportation, 2018. [24] Y.-A. Bennai, S. Yessad, and L. Bouallouche-Medjkoune, \"Aflexibleandadaptivemediumaccesscontrolprotocolfor improving quality of service in vehicular ad-hoc networks,\" International Journal of Computers and Applications, pp. 1- 10, 2021.

Copyright

Copyright © 2025 Pratik Khobragade, Pankaj Badipahadi , Alkesh Bagade, Hariom Nimburkar, Prof. Puneshkumar Tembhare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.