Probabilistic Trajectory Prediction with Structural Constraints
This work addresses trajectory prediction for autonomous systems by incorporating known rules, offering an incremental improvement over existing methods.
The paper tackles the problem of predicting motion trajectories for dynamic objects by combining probabilistic learning with constrained optimization to enforce structural constraints like collision avoidance, resulting in improved robustness and quality of trajectory distributions as demonstrated on real-world and simulated datasets.
This work addresses the problem of predicting the motion trajectories of dynamic objects in the environment. Recent advances in predicting motion patterns often rely on machine learning techniques to extrapolate motion patterns from observed trajectories, with no mechanism to directly incorporate known rules. We propose a novel framework, which combines probabilistic learning and constrained trajectory optimisation. The learning component of our framework provides a distribution over future motion trajectories conditioned on observed past coordinates. This distribution is then used as a prior to a constrained optimisation problem which enforces chance constraints on the trajectory distribution. This results in constraint-compliant trajectory distributions which closely resemble the prior. In particular, we focus our investigation on collision constraints, such that extrapolated future trajectory distributions conform to the environment structure. We empirically demonstrate on real-world and simulated datasets the ability of our framework to learn complex probabilistic motion trajectories for motion data, while directly enforcing constraints to improve generalisability, producing more robust and higher quality trajectory distributions.