论文标题
游戏理论运动计划中的多种假设相互作用
Multi-Hypothesis Interactions in Game-Theoretic Motion Planning
论文作者
论文摘要
我们提出了一种新颖的方法，用于处理动态游戏中非EGO玩家的意图的不确定性，并在自动驾驶汽车的运动计划中应用。在这些游戏中，平衡明确地说明了环境中其他代理商（例如驾驶员和行人）之间的相互作用。我们的方法通过构建有关场景中其他代理的目标和约束的多个假设来对其他代理的意图进行模型。对于每个候选假设，我们将代表该假设概率的Bernoulli随机变量关联，该变量可能与其他假设的概率无关。我们利用约束不对称和反馈信息模式以自然的方式结合了假设的概率。具体而言，将与给定假设相关的概率从$ 0 $增加到$ 1 $将避免碰撞的责任从假设的代理转移到了自我代理。这种方法允许生成自我代理的交互式轨迹，在这种轨迹中，自我表现出的自信或谨慎程度与它在场景上保持易于模型的不确定性直接相关。
We present a novel method for handling uncertainty about the intentions of non-ego players in dynamic games, with application to motion planning for autonomous vehicles. Equilibria in these games explicitly account for interaction among other agents in the environment, such as drivers and pedestrians. Our method models the uncertainty about the intention of other agents by constructing multiple hypotheses about the objectives and constraints of other agents in the scene. For each candidate hypothesis, we associate a Bernoulli random variable representing the probability of that hypothesis, which may or may not be independent of the probability of other hypotheses. We leverage constraint asymmetries and feedback information patterns to incorporate the probabilities of hypotheses in a natural way. Specifically, increasing the probability associated with a given hypothesis from $0$ to $1$ shifts the responsibility of collision avoidance from the hypothesized agent to the ego agent. This method allows the generation of interactive trajectories for the ego agent, where the level of assertiveness or caution that the ego exhibits is directly related to the easy-to-model uncertainty it maintains about the scene.
