Analysis highlights challenges in detection accuracy and proposes data fusion solutions for autonomous vehicles.
With the advancement of artificial intelligence (AI) technologies, object detection, data processing, and lidar navigation have become increasingly prevalent in autonomous driving, offering convenient transportation solutions. However, challenges such as low obstacle detection accuracy, high error rates, and frequent failures in emergency avoidance persist, necessitating further research into AI applications in autonomous driving. This paper explores the application scenarios of object detection, data processing, and navigation technologies in autonomous driving, compares the advantages and disadvantages of autonomous and human driving, and proposes a solution based on the fusion of data processing, visual navigation, and lidar navigation. The aim is to enhance the intelligence level of autonomous driving and provide theoretical support and practical references for the technology.
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Zhijie Zeng (2025) studied this question.
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