Multi-Hypothesis Interactions in Game-Theoretic Motion Planning
This addresses safety and efficiency in autonomous driving by handling intention uncertainty in dynamic games, though it is an incremental improvement over existing game-theoretic methods.
The paper tackles uncertainty about other agents' intentions in autonomous vehicle motion planning by modeling multiple hypotheses with probabilities, enabling the ego agent to adjust its assertiveness based on uncertainty, resulting in interactive trajectories that shift collision avoidance responsibility as probabilities change.
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.