Abstract
The ability to estimate human intentions and interact with human drivers intelligently is crucial for autonomous vehicles to successfully achieve their objectives. In this paper, we propose a game theoretic planning algorithm that models human opponents with an iterative reasoning framework and estimates human latent cognitive states through probabilistic inference and active learning. By modeling the interaction as a partially observable Markov decision process with adaptive state and action spaces, our algorithm is able to accomplish real-time lane changing tasks in a realistic driving simulator. We compare our algorithm's lane changing performance in dense traffic with a state-of-the-art autonomous lane changing algorithm to show the advantage of iterative reasoning and active learning in terms of avoiding overly conservative behaviors and achieving the driving objective successfully.
Abstract (translated)
人类的意图和智能地与人类司机互动对于无人驾驶汽车成功实现其目标至关重要。在本文中,我们提出了一个博弈论规划算法,通过迭代推理框架建模人类对手,并通过概率推断和主动学习估计人类的潜在认知状态。通过将互动建模为可部分观测的马尔可夫决策过程,并采用自适应的状态和行动空间,我们的算法能够在真实的驾驶模拟中实现实时车道切换任务。我们比较了我们的算法在稠密交通中的车道切换表现,与最先进的自主车道切换算法,以展示迭代推理和主动学习在避免过度保守行为并成功实现驾驶目标方面的优势。
URL
https://arxiv.org/abs/2301.09178
