Applications and Challenges of Machine Vision in Autonomous Vehicles

DOI

10.2991/978-94-6463-821-9_68How to use a DOI?

Keywords

Machine Vision; Autonomous Vehicles; Object Recognition

Abstract

The application of machine vision in autonomous vehicles has become a focal point of innovation, integrating advanced sensors and sophisticated image processing algorithms to redefine driving safety and comfort. High-definition cameras, 3D LiDAR, high-precision radars, and ultrasonic detectors are at the forefront of this technological revolution, providing autonomous vehicles with unparalleled capabilities. Machine vision contributes to depth estimation, obstacle detection, and lane recognition, forming the backbone of autonomous driving. However, the journey towards fully autonomous vehicles is fraught with challenges. Public acceptance of autonomous technology is one hurdle, but technical implementation presents numerous difficulties. Continuously optimizing algorithm performance is crucial for improving decision-making efficiency and accuracy. Additionally, diversifying and expanding datasets to include extreme scenarios enhances model versatility and reliability. Sensor technology innovation, striving for ultra-resolution performance, extends detection boundaries and strengthens anti-interference capabilities. Lastly, designing intuitive and clear human-machine interfaces ensures safe and stable vehicle control and feedback mechanisms. This study expounds the multimodal data fusion and intelligent algorithm collaborative optimization to achieve a systematic breakthrough in complex scene perception and decision-making and has important strategic value for reconstructing the future intelligent transportation ecology by improving the adaptability of extreme scenarios and the reliability of human-machine collaboration.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Minghao Tang
PY  - 2025
DA  - 2025/08/31
TI  - Applications and Challenges of Machine Vision in Autonomous Vehicles
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 702
EP  - 718
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_68
DO  - 10.2991/978-94-6463-821-9_68
ID  - Tang2025
ER  -