• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Using Eye Gaze to Enhance Generalization of Imitation Networks to Unseen Environments
【24h】

Using Eye Gaze to Enhance Generalization of Imitation Networks to Unseen Environments

机译：使用眼光来增强仿制网络的概念到看不见的环境

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Vision-based autonomous driving through imitation learning mimics the behavior of human drivers by mapping driver view images to driving actions. This article shows that performance can be enhanced via the use of eye gaze. Previous research has shown that observing an expert's gaze patterns can be beneficial for novice human learners. We show here that neural networks can also benefit. We trained a conditional generative adversarial network to estimate human gaze maps accurately from driver-view images. We describe two approaches to integrating gaze information into imitation networks: eye gaze as an additional input and gaze modulated dropout. Both significantly enhance generalization to unseen environments in comparison with a baseline vanilla network without gaze, but gaze-modulated dropout performs better. We evaluated performance quantitatively on both single images and in closed-loop tests, showing that gaze modulated dropout yields the lowest prediction error, the highest success rate in overtaking cars, the longest distance between infractions, lowest epistemic uncertainty, and improved data efficiency. Using Grad-CAM, we show that gaze modulated dropout enables the network to concentrate on task-relevant areas of the image.
机译：通过模仿学习的基于视觉的自主驾驶通过映射驱动器视图图像来模拟人类驱动程序的行为来驱动动作。本文显示，可以通过使用眼睛凝视来提高性能。以前的研究表明，观察专家的凝视模式可能对新手人类学习者有益。我们在这里展示神经网络也可以受益。我们培训了一个有条件的生成的对抗网络，从驾驶员视图图像准确地估计人的凝视图。我们描述了两种将凝视信息集成到模仿网络中的方法：眼睛注视作为额外的输入和凝视调制辍学。与无凝视的基线香草网络相比，无论如何显着增强了看不见的环境，但凝视调制的辍学表现更好。我们在单个图像和闭环测试中定量评估了性能，表明凝视调制辍学产生最低预测误差，超车的最高成功率，违规行为之间的最长距离，最低的认识性不确定性和提高数据效率。使用Grad-Cam，我们表明Gaze调制辍学使网络能够集中在图像的任务相关区域。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号