Abstract
Deep learning and computer vision techniques have become increasingly important in the development of self-driving cars. These techniques play a crucial role in enabling self-driving cars to perceive and understand their surroundings, allowing them to safely navigate and make decisions in real-time. Using Neural Networks self-driving cars can accurately identify and classify objects such as pedestrians, other vehicles, and traffic signals. Using deep learning and analyzing data from sensors such as cameras and radar, self-driving cars can predict the likely movement of other objects and plan their own actions accordingly. In this study, a novel approach to enhance the performance of selfdriving cars by using pre-trained and custom-made neural networks for key tasks, including traffic sign classification, vehicle detection, lane detection, and behavioral cloning is provided. The methodology integrates several innovative techniques, such as geometric and color transformations for data augmentation, image normalization, and transfer learning for feature extraction. These techniques are applied to diverse datasets,including the German Traffic Sign Recognition Benchmark (GTSRB), road and lane segmentation datasets, vehicle detection datasets, and data collected using the Udacity selfdriving car simulator to evaluate the model efficacy. The primary objective of the work is to review the state-of-the-art in deep learning and computer vision for self-driving cars. The findings of the work are effective in solving various challenges related to self-driving cars like traffic sign classification, lane prediction, vehicle detection, and behavioral cloning, and provide valuable insights into improving the robustness and reliability of autonomous systems, paving the way for future research and deployment of safer and more efficient self-driving technologies.
Abstract (translated)
深度学习和计算机视觉技术在自动驾驶汽车的发展中变得越来越重要。这些技术对于使自动驾驶汽车能够感知和理解周围环境，从而实现实时安全导航和决策起着关键作用。通过使用神经网络，自动驾驶汽车可以准确地识别并分类行人、其他车辆以及交通信号等物体。利用深度学习分析来自摄像头和雷达等传感器的数据，自动驾驶汽车可以预测其他对象的可能移动，并据此规划自己的行动。 这项研究提供了一种新的方法来增强自动驾驶汽车性能，该方法通过使用预训练及定制化神经网络处理关键任务，包括交通标志分类、车辆检测、车道识别以及行为克隆。该方法集成了多种创新技术，例如几何和颜色变换的数据增强、图像标准化以及迁移学习以提取特征等技术，并应用于各种数据集中，如德国交通标志识别基准（GTSRB）、道路和车道分割数据集、车辆检测数据集以及使用Udacity自动驾驶汽车模拟器收集的数据来评估模型的有效性。 研究的主要目标是回顾深度学习与计算机视觉在自动驾驶汽车领域的最新进展。这项工作的发现对于解决自动驾驶汽车面临的各种挑战，如交通标志分类、车道预测、车辆检测及行为克隆等非常有效，并且提供了关于提高自主系统的稳健性和可靠性的宝贵见解，为未来更安全和高效的自动驾驶技术的研究与部署铺平了道路。
URL
https://arxiv.org/abs/2603.09255
