Lane tracking in self-driving cars: leveraging TensorFlow for deep learning in image processing across localization, and sensor fusion
自动驾驶汽车的车道跟踪：利用TensorFlow进行跨定位和传感器融合的图像处理深度学习

摘要

车道追踪是自动驾驶汽车的关键组成部分，使其能够安全高效地在道路上行驶。本文探讨了强大的深度学习框架 TensorFlow 在图像处理中的车道追踪应用，重点介绍了其在定位和传感器融合方面的应用。自动驾驶汽车依靠大量传感器感知周围环境并做出明智的决策。其中，基于视觉的系统发挥着关键作用，因为它们能够提供有关道路环境的实时信息。深度学习技术，尤其是卷积神经网络 (CNN)，已被证明在处理视觉数据方面非常有效。TensorFlow 是一个流行的开源机器学习库，已成为实现此类网络的强大工具。本文探讨了如何利用 TensorFlow 进行车道追踪。本文深入探讨了用于检测和追踪车载摄像头拍摄图像中车道标记的 CNN 模型的开发。此外，本文还探讨了车道追踪与自动驾驶的两个关键环节——定位和传感器融合——的集成。准确的车道追踪对于车辆定位至关重要，因为它能够提供关键的位置信息。基于 TensorFlow 的模型可以通过持续更新车辆相对于检测到的车道的位置来提高定位精度。此外，传感器融合对于整合来自激光雷达、雷达和摄像头等各种传感器的信息至关重要。TensorFlow 有助于将车道跟踪数据与来自其他传感器的信息融合，从而增强汽车全面感知环境并做出安全驾驶决策的能力。

Abstract

Lane tracking is a critical component of self-driving cars, enabling them to navigate roads safely and efficiently. This article discusses the utilization of TensorFlow, a powerful deep learning framework, in the context of image processing for lane tracking, focusing on its application in localization and sensor fusion. Self-driving cars rely on a multitude of sensors to perceive their surroundings and make informed decisions. Among these, vision-based systems play a pivotal role, as they provide real-time information about the road environment. Deep learning techniques, particularly convolutional neural networks (CNNs), have proven to be highly effective in processing visual data. TensorFlow, a popular open-source machine learning library, has emerged as a robust tool for implementing such networks. This article explores how TensorFlow can be leveraged for lane tracking. It delves into the development of CNN models tailored to detect and track lane markings in images captured by onboard cameras. Furthermore, the integration of lane tracking into two key aspects of autonomous driving: localization and sensor fusion. Accurate lane tracking is crucial for vehicle localization, as it provides critical positional information. TensorFlow-based models can contribute to improved localization accuracy by continuously updating the vehicle's position relative to the detected lanes. Additionally, sensor fusion is essential for consolidating information from diverse sensors like LiDAR, radar, and cameras. TensorFlow facilitates the fusion of lane tracking data with information from other sensors, enhancing the car's ability to perceive its environment comprehensively and make safe driving decisions.