Applied and Computational Engineering

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Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Series Vol. 52 , 27 March 2024


Open Access | Article

Sensor and sensor fusion technology in autonomous vehicles

Bo Duan * 1
1 Taiyuan University of Technology

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 52, 132-137
Published 27 March 2024. © 27 March 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Bo Duan. Sensor and sensor fusion technology in autonomous vehicles. ACE (2024) Vol. 52: 132-137. DOI: 10.54254/2755-2721/52/20241470.

Abstract

The perception and navigation of autonomous vehicles heavily rely on the utilization of sensor technology and the integration of sensor fusion techniques, which play an essential role in ensuring a secure and proficient understanding of the vehicle's environment.This paper highlights the significance of sensors in autonomous vehicles and how sensor fusion techniques enhance their capabilities. Firstly, the paper introduces the different types of sensors commonly used in autonomous vehicles and explains their principles of operation, strengths, and limitations in capturing essential information about the vehicle’s environment. Next, the paper discusses various sensor fusion algorithms, such as Kalman filters and particle filters. Furthermore, the paper explores the challenges associated with sensor fusion and addresses the issue of handling sensor failures or uncertainties. The benefits of sensor fusion technology in autonomous vehicles are also presented. These include improved perception of the environment, enhanced object recognition and tracking, better trajectory planning, and enhanced safety through redundancy and fault tolerance. Lastly, the paper discusses the advancements and highlights the integration of artificial intelligence and machine learning techniques to optimize sensor fusion algorithms and improve the overall autonomy of the vehicle. Following thorough analysis, the deduction can be made that sensor and sensor fusion technology assume a critical function in facilitating efficient and secure autonomous vehicle navigation within intricate surroundings.

Keywords

Sensors, Autonomous vehicles, Sensor fusion, Camera, Lidar

References

1. T. Kanade, "Autonomous land vehicle project at CMU", CSC '86 Proceedings of the 1986 ACM fourteenth annual conference on Computer science, 1986.

2. R. Wallace, First results in robot road-following, JCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence, 1985.

3. E. D. Dickmanns, A. Zapp, Autonomous High Speed Road Vehicle Guidance by Computer Vision, IFAC Proceedings Volumes, 1987, 20.5: 221-226.

4. S. Thrun et al., Stanley: The Robot That Won the DARPA Grand Challenge, Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Volume 23 Issue 9, pp. 661-692, 2006.

5. M. Montemerlo at al., Winning the DARPA grand challenge with an AI robot, Proc. of the 21st national conference on Artificial intelligence, pp. 982-987, July 2006.

6. Martin Buehler, Karl Iagnemma, Sanjiv Singh, The DARPA Urban Challenge: Autonomous Vehicles in City Traffic. Springer Tracts in Advanced Robotics, 2009.

7. ZIEGLER J, BENDER P, SCHREIBER M, et al. Making bertha drive: an autonomous journey on a historic route [J]. IEEE Intelligent Transportation Systems Magazine, 2014, 6(2): 8. DOI:10.1109/MITS.2014.2306552

8. M. Bojarski et al., End to end learning for self-driving cars, 2016. Available: https://arxiv.org/abs/1604.07316.

9. J. Kocić, N. Jovičić and V. Drndarević, Driver behavioral cloning using deep learning, 2018 17th International Symposium INFOTEH-JAHORINA (INFOTEH), East Sarajevo, 2018, pp. 1-5.

10. J. Kocić, N. Jovičić and V. Drndarević, End-to-End Autonomous Driving using a Depth-Performance Optimal Deep Neural Network, 2018, submitted for publication.

11. M. Riedmiller, M. Montemerlo and H. Dahlkamp, Learning to Drive a Real Car in 20 Minutes, 2007 Frontiers in the Convergence of Bioscience and Information Technologies, Jeju City, 2007, pp. 645-650.

12. L. Fridman, B. Jenik, J. Terwilliger, DeepTraffic: Driving Fast through Dense Traffic with Deep Reinforcement Learning, arXiv:1801.02805 [cs.NE], Jan. 2018. Available at: https://arxiv.org/abs/1801.02805.

13. Kuutti, S.; Bowden, R.; Jin, Y.; Barber, P.; Fallah, S. A Survey of Deep Learning Applications to Autonomous Vehicle Control. IEEE Trans. Intell. Transp. Syst. 2021, 22, 712–733.

14. Joglekar, A.; Joshi, D.; Khemani, R.; Nair, S.; Sahare, S. Depth Estimation Using Monocular Camera. IJCSIT 2011, 2, 1758–1763.

15. Bhoi, A. Monocular Depth Estimation: A Survey. arXiv 2019, arXiv:1901.09402v1.

16. Garg, R.; Wadhwa, N.; Ansari, S.; Barron, J.T. Learning Single Camera Depth Estimation using Dual-Pixels. arXiv 2019, arXiv:1904.05822v3.

17. Shahian Jahromi, B.; Tulabandhula, T.; Cetin, S. Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles. Sensors 2019, 19, 4357.

18. Wang, Z.; Wu, Y.; Niu, Q. Multi-Sensor Fusion in Automated Driving: A Survey. IEEE Access 2019, 8, 2847–2868.

19. Detecting Static Objects in View Using—Electrical Engineering Stack Exchange. Available online:https://electronics.stackexchange.com/questions/236484/detecting-static-objects-in-view-using-radar (accessed on 29 December 2020).

20. Campbell, S.; O’Mahony, N.; Krpalcova, L.; Riordan, D.; Walsh, J.; Murphy, A.; Conor, R. Sensor Technology in Autonomous Vehicles: A review. In Proceedings of the 2018 29th Irish Signals and Systems Conference (ISSC), Belfast, UK, 21–22 June 2018.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 4th International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-83558-349-4
ISBN (Online)
978-1-83558-350-0
Published Date
27 March 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/52/20241470
Copyright
27 March 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

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