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首页> 外文学位 >Towards Human-Like Automated Driving: Learning Spacing Profiles from Human Driving Data.
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Towards Human-Like Automated Driving: Learning Spacing Profiles from Human Driving Data.

机译：迈向类似于人类的自动驾驶：从人类驾驶数据中学习间距特征。

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摘要

For automated driving vehicles to be accepted by their users and safely integrate with traffic involving human drivers, they need to act and behave like human drivers. This not only involves understanding how the human driver or occupant in the automated vehicle expects their vehicle to operate, but also involves how other road users perceive the automated vehicle's intentions. This research aimed at learning how drivers space themselves while driving around other vehicles. It is shown that an optimized lane change maneuver does create a solution that is much different than what a human would do. There is a need to learn complex driving preferences from studying human drivers.;This research fills the gap in terms of learning human driving styles by providing an example of learned behavior (vehicle spacing) and the needed framework for encapsulating the learned data. A complete framework from problem formulation to data gathering and learning from human driving data was formulated as part of this research. On-road vehicle data were gathered while a human driver drove a vehicle. The driver was asked to make lane changes for stationary vehicles in his path with various road curvature conditions and speeds. The gathered data, as well as Learning from Demonstration techniques, were used in formulating the spacing profile as a lane change maneuver. A concise feature set from captured data was identified to strongly represent a driver's spacing profile and a model was developed. The learned model represented the driver's spacing profile from stationary vehicles within acceptable statistical tolerance. This work provides a methodology for many other scenarios from which human-like driving style and related parameters can be learned and applied to automated vehicles.
机译：为了使自动驾驶车辆受到其用户的接受并与涉及驾驶员的交通安全集成，他们需要像驾驶员一样行动。这不仅涉及了解自动驾驶车辆中的驾驶员或乘员如何期望他们的车辆运行，而且还涉及其他道路使用者如何感知自动驾驶车辆的意图。这项研究旨在了解驾驶员如何在其他车辆周围行驶时如何进行自我调节。结果表明，优化的换道策略确实创造了与人类所要解决的方案大不相同的解决方案。有必要通过研究人类驾驶员来学习复杂的驾驶偏好。这项研究通过提供学习行为的示例（车辆间距）和封装学习数据所需的框架，填补了学习人类驾驶方式方面的空白。这项研究的组成部分是从问题制定到数据收集以及从人类驾驶数据学习的完整框架。在驾驶员驾驶车辆时收集公路车辆数据。要求驾驶员以各种道路曲率条件和速度改变其行驶路径中的固定车辆的车道。所收集的数据以及“从演示中学习”技术被用于将间距轮廓公式化为车道变更操纵。从捕获的数据中确定了一个简洁的功能集，以强烈代表驾驶员的空间分布，并开发了一个模型。学习的模型表示驾驶员在固定的统计公差范围内与固定车辆的间距轮廓。这项工作为许多其他场景提供了一种方法，从中可以学习类似人的驾驶风格和相关参数，并将其应用于自动驾驶汽车。

著录项

  • 作者

    Ali, Syed.;

  • 作者单位

    Wayne State University.;

  • 授予单位 Wayne State University.;
  • 学科 Automotive engineering.;Artificial intelligence.;Computer engineering.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 138 p.
  • 总页数 138
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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