• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Consumer Electronics - Taiwan >Deep Reinforcement Learning for Collision Avoidance of Autonomous Vehicle
【24h】

Deep Reinforcement Learning for Collision Avoidance of Autonomous Vehicle

机译：深度强化学习避免自动驾驶汽车的碰撞

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To save training efforts, reinforcement learning approach is applied to the autonomous vehicle for obstacle avoidance. Therefore, this study is aimed to let the autonomous vehicle to learn from mistakes and readdress its movement accuracy for collision avoidance in working environment. An enhanced learning method Q-learning is used to record and update the Q values for different movement through a table that the autonomous vehicle can use it to determine how and where to move. The Q table is learned through the deep learning neural network which may encounter innumerable situations from the environments and the different actions performed by the autonomous vehicle. In the experiments, the depth camera is adopted as the input device to be not affected by light intensity and road color. The Q table is ready to use after 9000 epochs or about 3.5 hours training. Let the autonomous vehicle run for 3 minutes at a time in three different environments with lights on and off 10 times each. The success rate of obstacle avoidance is as high as 95% which proves the feasibility of proposed approach.
机译：为了节省训练精力，强化学习方法被应用于自动驾驶汽车，以避开障碍物。因此，本研究旨在让自动驾驶汽车从错误中学习，并重新解决其运动精度，从而避免在工作环境中发生碰撞。增强型学习方法Q学习用于通过表格记录和更新不同运动的Q值，自动驾驶汽车可以使用该表来确定如何以及在何处运动。 Q表是通过深度学习神经网络学习的，该神经网络可能会遇到来自环境和自动驾驶汽车执行的不同动作的无数情况。在实验中，采用深度相机作为输入设备，不受光强和道路颜色的影响。在经过9000个历时或大约3.5个小时的训练后，Q表即可使用。让自动驾驶汽车在三种不同的环境下一次开启3分钟，每次开启和关闭10次。避障成功率高达95％，证明了该方法的可行性。

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