在飞行中解决不确定性:模拟自适应驾驶行为作为主动推理.
Johan Engström1, Ran Wei2, Anthony D McDonald3
1Waymo LLC, Mountain View, CA, United States.
Frontiers in neurorobotics
|April 5, 2024
概括
这项研究引入了适应性人类驾驶行为的新计算模型,解释了驾驶员如何管理不确定性. 积极推断模型将预期的自由能量最小化,以平衡进步和谨慎,为自动驾驶汽车开发提供一种可解释的方法.
科学领域:
- 计算神经科学是一种神经科学.
- 交通心理学 交通心理学
- 人与计算机的交互
背景情况:
- 开发精确的模拟人类驾驶员模型对于自动驾驶汽车 (AV) 的开发和评估至关重要.
- 现有的模型往往缺乏计算严谨性,是特定于场景的,或者是缺乏可解释性的黑盒.
- 需要对适应性人类驾驶行为的可概括,可解释的计算模型.
研究的目的:
- 为适应人类驾驶行为提出一种新的,可解释的计算模型.
- 解释司机如何通过整合目标寻找和信息寻找行为来管理不确定性.
- 证明模型在需要不确定性管理的各种驾驶场景中的适用性.
主要方法:
- 开发了一个基于主动推理的计算模型,这是计算神经科学的一个框架.
- 制定了自适应驾驶作为政策选择过程,尽量减少预期的自由能量.
- 将模型应用于模拟的驾驶场景,其中包括闭塞和视觉时间共享.
主要成果:
- 积极推断模型成功地解释了进步与驾驶时谨慎之间的权衡.
- 该模型展示了如何将不确定性解决方案整合到寻求目标的行为中.
- 类似人类的自适应驾驶行为出现在复杂场景的模拟中.
结论:
- 积极推断为建模适应性人类驾驶提供了一个原则和可解释的框架.
- 拟议的模型提供了一种统一的方法来理解以目标为导向和解决不确定性的驾驶行为.
- 这项工作促进了AV研究更现实的和可解释的人类驾驶模型的开发.
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