Trajectory Prediction via Bayesian Intention Inference under Unknown Goals and Kinematics

Abstract--This work introduces an adaptive Bayesian algorithm for real-time trajectory prediction via intention inference, where a target's intentions and motion characteristics are unknown and subject to change. The method concurrently estimates two critical variables: the target's current intention, modeled as a Markovian latent state, and an intention parameter that describes the target's adherence to a shortest-path policy. By integrating this joint update technique, the algorithm maintains robustness against abrupt intention shifts and unknown motion dynamics. A sampling-based trajectory prediction mechanism then exploits these adaptive estimates to generate probabilistic forecasts with quantified uncertainty. Experimental results demonstrate that the proposed approach significantly outperforms non-adaptive and partially adaptive methods. The method operates in real time around 270 Hz without requiring training or detailed prior knowledge of target behavior, showcasing its applicability in various robotic systems. Real-world autonomous systems such as self-driving cars, service robots, and surveillance drones frequently face intention inference tasks [1]: they must determine what another agent or human is trying to achieve and where it is likely to go next [2], [3]. These tasks are inherently challenging for several reasons. First, the target's motion dynamics are often unknown. For example, a pedestrian may switch between walking, jogging, or stopping unpredictably. Second, the agent's intention may shift during execution, such as changing to a new goal without any observable signal, i.e., in a non-cooperative fashion.