Automatic Driving Strategy based on Machine Vision: Review

DOI

10.2991/978-94-6463-864-6_46How to use a DOI?

Keywords

Environment Sensing; Path Planning; Multi-Sensor Fusion

Abstract

With the growth of the number of automobiles, traffic safety problems are highlighted, and automatic driving technology becomes an important means of solution. Machine vision, as the key perception technology of automatic driving, acquires image data through vehicle-mounted cameras to provide a basis for decision-making. Its application in automatic driving is in environment perception and detection, path planning and decision making, and multi-sensor fusion. Among them, 3D object detection is the key technology in environment sensing. In path planning and decision making, uncertainty prediction and environment-aware motion planning are research hotspots. Multi-sensor fusion improves the accuracy and robustness of the perception system. However, machine vision still faces many challenges, such as lack of reliability in bad weather and light conditions, adaptability in complex traffic environments, and difficulties in multi-sensor data fusion. In the future, machine vision will increasingly emphasise interdisciplinary integration within the realm of autonomous driving. The amalgamation of deep learning, multi-sensor fusion, advancements in hardware technology, and the evolution of V2X communication technology will elevate the development of autonomous driving technology, thereby offering technical support for achieving safer and more convenient autonomous driving.

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  - Yueran Cao
PY  - 2025
DA  - 2025/10/23
TI  - Automatic Driving Strategy based on Machine Vision: Review
BT  - Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)
PB  - Atlantis Press
SP  - 502
EP  - 513
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-864-6_46
DO  - 10.2991/978-94-6463-864-6_46
ID  - Cao2025
ER  -