LiDAR Point Clouds in Autonomous Driving Integrated with Deep Learning: A Tech Prospect

The combination of LiDAR point clouds and deep learning is transforming autonomous systems. The balance between LiDAR technology and DL methods for accurate point cloud categorization is the topic of this work. Our objectives involve generating effective algorithms for real-time processing, providing strength in a variety of contexts, and improving safety in applications like autonomous driving. The main issues addressed involve LiDAR point clouds, deep learning broad overviews, and data preprocessing approaches. Real-world examples demonstrate effective uses beyond autonomous driving, demonstrating the technology’s adaptability. Although there are difficulties, continuing research suggests responses for computing efficiency and model resilience. The conclusion predicts a bright future with versatile and adaptive technologies for wide-scale implementation across developing LiDAR environments.