Simultaneous Intent Prediction and State Estimation Using an Intent-Driven Intrinsic Coordinate Model

Abstract

The motion of an object (e.g. ship, jet, pedestrian, bird, drone, etc.) is usually governed by premeditated actions as per an underlying intent, for instance reaching a destination. In this paper, we introduce a novel intent-driven dynamical model based on a continuous-time intrinsic coordinate model. By combining this model with particle filtering, a seamless approach for jointly predicting the destination and estimating the state of a highly manoeuvrable object is developed. We examine the proposed inference technique using real data with different measurement models to demonstrate its efficacy. In particular, we show that the introduced approach can be a flexible and competitive alternative, in terms of prediction and estimation performance, to other existing methods for various measurement models including nonlinear ones.

A Deep Mixture of Experts Network for Drone Trajectory Intent Classification and Prediction using Non-Cooperative Radar Data

“…The limitations of model-based approaches can be improved through the development of intent-driven dynamical models [4], [6] that predicts both the future kinematics and the intended destination. In these approaches, the intent is modelled as a discrete set of destined locations [6] or as a continuous space over a designated region. Here, the intent is defined as whether the drone intends to enter to an unauthorized region during the flight [6].…”

“…In these approaches, the intent is modelled as a discrete set of destined locations [6] or as a continuous space over a designated region. Here, the intent is defined as whether the drone intends to enter to an unauthorized region during the flight [6]. However, these approaches do not leverage existing patterns and insights derived from past data, e.g., nearby routes, manoeuvres or flight profile characteristics.…”