Image Processing Techniques for Autonomous Vehicles Including Lane Detection and Obstacle Avoidance

Abstract

This chapter explores the pivotal role of image processing techniques in the development of autonomous vehicles, with a specific focus on lane detection and obstacle avoidance. As the automotive industry increasingly integrates advanced deep learning models, the necessity for efficient data preparation, quality assurance, and real-time implementation becomes paramount. The chapter delves into various deep learning architectures, particularly Convolutional Neural Networks (CNNs) and Residual Networks (ResNets), highlighting their effectiveness in accurately identifying lane markings under diverse driving conditions. Additionally, the integration of edge computing was discussed, emphasizing its advantages in reducing latency and enhancing bandwidth efficiency, critical for real-time decision-making. Furthermore, this chapter addresses the importance of data quality, providing insights into preparation and augmentation techniques essential for training robust models. By examining these key areas, this chapter contributes to the broader understanding of the challenges and opportunities in the application of deep learning for autonomous vehicle technology.