Abstract
The vehicle detection system is an essential part of autonomous navigation systems. In this paper, the investigation on the vehicle detection system is conducted by integrating the processed data from the ultrasonic sensor, together with the collected data from the Pixy sensor. The ultrasonic sensor is used as obstacle detection sensors with the optimal performances, while the Pixy camera is used as the vision-based sensor device. The data obtained from both sensors are processed and compared to each other. Results received from both data comparisons conclude the current condition as the safety status of the car was being determined and delivered to the user. In other words, the sensors’ data were integrated to validate the accuracy of the information received by the controller to navigate the vehicle. The Pixy camera detects the vehicle in the range of camera view. On the other hand, for the ultrasonic sensor, three stages of distance detection level are being measured. The performance of the proposed method is demonstrated by experimenting statically several times on a prototype scale.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bécsi T, Aradi S, Fehér Á, Gáldi G (2017) Autonomous vehicle function experiments with low-cost environment sensors. In: 20th EURO working group on transportation meeting, EWGT 2017, 4–6 Septe 2017, Budapest, Hungary, pp 333–340
Jo K, Kim J, Kim D, Jang C, Sunwoo M (2014) Development of autonomous car—part I: distributed system architecture and development process. IEEE Trans Ind Electron 61(12):7131–7141
Iqbal A, Ahmed SS, Tauqeer MD, Sultan A, Abbas SY (2017) Design of multifunctional autonomous car using ultrasonic and infrared sensors. In: 2017 International symposium on wireless systems and networks (ISWSN), pp 1–5
Li Q, Zheng N, Cheng H (2004) Springrobot: a prototype autonomous vehicle and its algorithms for lane detection. IEEE Trans Intell Transp Syst 5(4):300–309
Mustapha B, Zayegh A, Begg RK (2013) Ultrasonic and infrared sensors performance in a wireless obstacle detection system. In: 2013 First international conference on artificial intelligence, modelling and simulation, pp 1–6
Rathod SM, Apte S (2019) Obstacle detection using sensor based system for an four wheeled autonomous electric robot. In: Proceedings of the fourth international conference on communication and electronics systems (ICCES 2019), pp 493–497
Kim C-H, Lee T-J, Cho D-I (2018) An application of stereo camera with two different FoVs for SLAM and obstacle detection. IFAC PapersOnLine 51–22:148–153
Lekic V, Babic Z (2019) Automotive radar and camera fusion using generative adversarial networks. Comput Vis Image Underst 1–8
Jha H, Lodhi V, Chakravarty D (2019) Object detection and identification using vision and radar data fusion system for ground-based navigation. In: 2019 6th International conference on signal processing and integrated network (SPIN), pp 590–594
Gruyer D, Cord A, Belaroussi R (2013) Vehicle detection and tracking by collaborative fusion between laser scanner and camera. In: 2013 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 5207–5215
Saddam (2020) Arduino & ultrasonic sensor based distance measurement, 27 June 2015. (Online). Available: https://circuitdigest.com/microcontroller-projects/arduino-ultrasonic-sensor-based-distance-measurement. Accessed 15 July 2020
Sundarajoo S, Abdul Ghani AS (2018) Improvement of auto-tracking mobile robot based on HSI color model. Indonesian J Electr Eng Comput Sci 12(3):1349–1357. ISSN 2502-4752. http://dx.doi.org/10.11591/ijeecs.v12.i3.pp1349-1357
Abdul Ghani AS, Mat Isa NA (2015) Homomorphic filtering with image fusion for enhancement of details and homogeneous contrast of underwater image. Ind J Geo-Mar Sci (IJMS) 44(12):1904–1919. ISSN: 0975-1033
Acknowledgements
The authors would like to thanks all reviewers. This work is supported by the internal Universiti Malaysia Pahang Research Grant RDU1903140 entitled ‘Analyzing the Effects of Dark Channel Fusion and Pixels Distribution Shifting on Image Histogram for Development of Remotely Surface Vehicle.’
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bin Abd Rashid, M., Abdul Ghani, A. (2022). Investigation on Integration of Sensors and Vision-Based Vehicle Detection System for Autonomous Vehicle. In: Ab. Nasir, A.F., Ibrahim, A.N., Ishak, I., Mat Yahya, N., Zakaria, M.A., P. P. Abdul Majeed, A. (eds) Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering, vol 730. Springer, Singapore. https://doi.org/10.1007/978-981-33-4597-3_71
Download citation
DOI: https://doi.org/10.1007/978-981-33-4597-3_71
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4596-6
Online ISBN: 978-981-33-4597-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)