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.

中文翻译：

---

博弈论运动规划中的多假设交互

我们提出了一种处理动态游戏中非自我玩家意图的不确定性的新方法，并将其应用于自动驾驶汽车的运动规划。这些游戏中的均衡明确说明了环境中其他代理之间的交互，例如司机和行人。我们的方法通过构建关于场景中其他代理的目标和约束的多个假设来模拟其他代理意图的不确定性。对于每个候选假设，我们关联一个表示该假设概率的伯努利随机变量，该变量可能独立于其他假设的概率，也可能不独立。我们利用约束不对称和反馈信息模式以自然的方式合并假设的概率。具体来说，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代理转移到自我代理。这种方法允许为自我代理生成交互式轨​​迹，其中自我表现出的自信或谨慎程度与其对场景保持的易于建模的不确定性直接相关。