• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 40, Pages: 2056-2065

Original Article

Advanced Driving Assistance System for Cars Using Raspberry Pi

Received Date:13 August 2022, Accepted Date:16 September 2022, Published Date:18 October 2022


Objectives : Hardware implementation of advanced driving assistance system which can be able to identify i). Lane detection and assist system. ii). Blind spot detection and warning system (BSDW). iii). Forward collision and warning system (FCWS). iv). Pedestrian detection system. The primary goal of the developed system is to identify the above features in order to prevent accidents on the road and ensure pedestrian safety.  The suggested method uses a canny edges detectiMethods:on algorithm is used to detect road edges. The input to this system is images captured by the camera with the help of the Open CV library a python image processing algorithm is created that tracks the lane. Histogram of Orientation (HOG) using the sliding window method is used for pedestrian detection. The control unit for the proposed system is Raspberry Pi module 3B, JSN-SR04T ultrasonic sensor and HC-SR04 ultrasonic sensor has been used for (BSDW) system and (FCWS) respectively. Findings: Results demonstrate that the suggested technique can accurately recognize both straight and curved lanes using edge detection algorithm, and also able to identify vehicles in Blind spot area. Novelty: This technology has a high demand in the automotive industry and the system can be implemented in all the future cars which can able to reduce the accident rates.

Keywords: Adaptive Cruse Control; Blind Spot Detection; Autonomous Driving Assistance system; Lane Detection System; Forward Collision; Pedestrian detection; OpenCV


  1. Haghani M, Behnood A, Oviedo-Trespalacios O, Bliemer MCJ. Structural anatomy and temporal trends of road accident research: Full-scope analyses of the field. Journal of Safety Research. 2021;79:173–198. Available from: https://doi.org/10.1016/j.jsr.2021.09.002
  2. Shetty NA, Mohan K, Kaushik K. Autonomous Self-Driving Car using Raspberry Pi Model. www.ijert.org RTESIT - 2019 Conference Proceedings. 2019;p. 2278–0181. Available from: https://www.ijert.org/Autonomous-Self-Driving-Car-using-Raspberry-Pi-Model
  3. Zhong Z, Hu Z, Guo S, Zhang X, Zhong Z, Ray B. Detecting multi-sensor fusion errors in advanced driver-assistance systems. Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis. 2022. Available from: https://doi.org/10.1145/3533767.3534223
  4. Adnan Z, Hassan MZ. Vehicle Blind Spot Monitoring Phenomenon using Ultrasonic Sensor”. International Journal of Emerging Trends in Engineering Research. 2020;8(8). Available from: https://doi.org/10.30534/ijeter/2020/50882020
  5. Panda L, Mohanty BP. RECENT DEVELOPMENTS IN LANE DEPARTURE WARNING SYSTEM: AN ANALYSIS. Ethics and Information Technology (ETIT). 2020. Available from: http://doi.org/10.26480/etit.02.2020.151.153
  6. Dswilan HS, Abubakar. Flood monitoring system using ultrasonic sensor SN-SR04T and SIM 900A. Journal of Physics Conference Series. 2021. Available from: https://doi.org/10.1088/1742-6596/1876/1/012003
  7. Alamsyah 7SA, Purwanto D, Attamimi M. Lane detection using edge detection and spatio-temporal incrimental clustring. 2021 International seminar on Intelligent technology and its application. 2021. Available from: https://doi.org/10.1109/ISITIA52817.2021.9502232
  8. Neuhuber N, Pretto P, Kubicek B. Interaction strategies with advanced driver assistance systems. Transportation Research Part F: Traffic Psychology and Behaviour. 2022;88:223–235. Available from: https://doi.org/10.1016/j.trf.2022.05.013
  9. Siddiqi MH, Alrashdi I. Sharpening and Detection of Road Studs for Intelligent Vehicles at Nighttime. Security and Communication Networks. 2022;2022:1–10. Available from: https://doi.org/10.1155/2022/9329715
  10. Muzammel M, Yusoff MZ, Saad MNM, Sheikh F, Awais MA. Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture. Sensors. 2022;22(16):6088. Available from: https://doi.org/10.3390/s22166088
  11. Zhang X, Chen F. Lane Line Edge Detection Based on Improved Adaptive Canny Algorithm. Journal of Physics: Conference Series. 2020;1549(2):022131. Available from: https://doi.org/10.1088/1742-6596/1549/2/022131
  12. Viswanatha V. Intelligent line follower robot using MSP430G2ET for industrial applications. Helix-The Scientific Explorer| Peer Reviewed Bimonthly International Journal. 2020;10:232–237. Available from: https://helixscientific.pub/index.php/Home/article/view/136/136
  13. Viswanatha V, Reddy RVS. Digital control of buck converter using arduino microcontroller for low power applications. 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon). 2017;p. 439–443. Available from: https://ieeexplore.ieee.org/document/8358412
  14. Viswanatha V, Reddy RVS, Rajeswari. Characterization of analog and digital control loops for bidirectional buck–boost converter using PID/PIDN algorithms. Journal of Electrical Systems and Information Technology. 2020;7(1):1–25. Available from: https://jesit.springeropen.com/articles/10.1186/s43067-020-00015-6


© 2022 et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.