论文标题
人类驾驶员行为预测的在线参数估计
Online Parameter Estimation for Human Driver Behavior Prediction
论文作者
论文摘要
驾驶员模型对于规划自动驾驶汽车以及验证其模拟安全性是无价的。高度参数化的黑框驱动程序模型非常表现力，可以捕获细微的行为。但是，它们通常缺乏解释性，有时甚至表现出不现实的危险行为。基于规则的模型是可以解释的，并且可以设计以保证“安全”的行为，但由于其参数数量较少而表现不佳。在本文中，我们表明，在线参数估计应用于智能驱动程序模型，可捕获细微的个人驾驶行为，同时提供无冲突的轨迹。我们使用粒子过滤解决了在线参数估计问题，并在两个现实世界驾驶数据集上针对基于规则和黑框驱动程序模型进行基准测试性能。我们评估驾驶员模型与地面真相数据演示的亲密关系，并评估由此产生的紧急驾驶行为的安全性。
Driver models are invaluable for planning in autonomous vehicles as well as validating their safety in simulation. Highly parameterized black-box driver models are very expressive, and can capture nuanced behavior. However, they usually lack interpretability and sometimes exhibit unrealistic-even dangerous-behavior. Rule-based models are interpretable, and can be designed to guarantee "safe" behavior, but are less expressive due to their low number of parameters. In this article, we show that online parameter estimation applied to the Intelligent Driver Model captures nuanced individual driving behavior while providing collision free trajectories. We solve the online parameter estimation problem using particle filtering, and benchmark performance against rule-based and black-box driver models on two real world driving data sets. We evaluate the closeness of our driver model to ground truth data demonstration and also assess the safety of the resulting emergent driving behavior.
