Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles
This addresses a critical safety issue for autonomous vehicles by improving prediction accuracy in complex, occluded environments, though it is an incremental advancement over existing inverse-planning methods.
The paper tackles the problem of goal recognition for autonomous vehicles when unseen obstacles or occluded factors influence observed behavior, introducing the GOFI algorithm that jointly infers goals and occluded factors using inverse-planning and integrates them into MCTS, resulting in more accurate beliefs and safer navigation that avoids collisions in scenarios where baselines fail.
Recognising the goals or intentions of observed vehicles is a key step towards predicting the long-term future behaviour of other agents in an autonomous driving scenario. When there are unseen obstacles or occluded vehicles in a scenario, goal recognition may be confounded by the effects of these unseen entities on the behaviour of observed vehicles. Existing prediction algorithms that assume rational behaviour with respect to inferred goals may fail to make accurate long-horizon predictions because they ignore the possibility that the behaviour is influenced by such unseen entities. We introduce the Goal and Occluded Factor Inference (GOFI) algorithm which bases inference on inverse-planning to jointly infer a probabilistic belief over goals and potential occluded factors. We then show how these beliefs can be integrated into Monte Carlo Tree Search (MCTS). We demonstrate that jointly inferring goals and occluded factors leads to more accurate beliefs with respect to the true world state and allows an agent to safely navigate several scenarios where other baselines take unsafe actions leading to collisions.