论文标题
模仿人赛车行为的概率框架
A Probabilistic Framework for Imitating Human Race Driver Behavior
论文作者
论文摘要
了解和建模人类驾驶员行为对于先进的车辆开发至关重要。但是，独特的驾驶方式，不一致的行为和复杂的决策过程使其成为一项艰巨的任务，现有方法通常缺乏可变性或鲁棒性。为了解决此问题，我们提出了驱动程序行为（Promod）的概率建模，该模块化框架将驱动程序行为建模的任务分为多个模块。通过概率运动原始素，用衣服的轨迹来学习全球目标轨迹分布，用于局部路径生成，相应的动作选择是由神经网络执行的。与其他模仿学习算法相比，模拟赛车设置中的实验在模仿准确性和鲁棒性方面具有相当大的优势。提出的框架的模块化体系结构促进了驱动线适应和测序多运动原语的直接可扩展性，以供将来的研究。
Understanding and modeling human driver behavior is crucial for advanced vehicle development. However, unique driving styles, inconsistent behavior, and complex decision processes render it a challenging task, and existing approaches often lack variability or robustness. To approach this problem, we propose Probabilistic Modeling of Driver behavior (ProMoD), a modular framework which splits the task of driver behavior modeling into multiple modules. A global target trajectory distribution is learned with Probabilistic Movement Primitives, clothoids are utilized for local path generation, and the corresponding choice of actions is performed by a neural network. Experiments in a simulated car racing setting show considerable advantages in imitation accuracy and robustness compared to other imitation learning algorithms. The modular architecture of the proposed framework facilitates straightforward extensibility in driving line adaptation and sequencing of multiple movement primitives for future research.
