Bayesian Intent Prediction for Fast Maneuvering Objects using Variable Rate Particle Filters

Abstract

The motion of a tracked object often has long term underlying dependencies due to premeditated actions dictated by intent, such as destination. Revealing this intent, as early as possible, can enable advanced intelligent system functionalities for conflict/opportunity detection and automated decision making, for instance in surveillance and human computer interaction. This paper presents a novel Bayesian intent inference framework that utilises sequential Monte Carlo (SMC) methods to determine the destination of a tracked object exhibiting unknown jump behaviour. The latter can arise from the object undertaking fast maneuvers (e.g. for obstacle avoidance) and/or due to external uncontrollable environmental perturbations. Suitable intent-driven stochastic models and inference routines are introduced. The effectiveness of the proposed approach is demonstrated using synthetic and real data.

Modeling intent and destination prediction within a Bayesian framework: Predictive touch as a usecase

“…The latter parameters can be estimated from a small number of example motion patterns or trajectories. Linear Time-Invariant Gaussian systems were considered in the aforementioned papers and more recently nonlinear behavior due to external forces (e.g., jumps and jolts in the pointing movements due to the road/driving conditions) was briefly addressed in Gan et al (2019). Here and compared with previous work, we 1. present an overview and unified treatment of the intent prediction task for linear as well as nonlinear (albeit within a conditionally linear formulation) motion models and systems, 2. propose a new approach to the bridging distributions (BD) class of intent-driven models, which have a moderate computational requirement and a clear stochastic interpretation.…”

“…Different orders of kinematics included in each "substate" x t,i,s along with the corresponding parameters lead to distinct SDEs as per (3), for instance: (a) the mean reverting diffusion (MRD) model which only includes position in the state (Ahmad et al, 2016b), (b) equilibrium reverting velocity (ERV) that model position and velocity (Ahmad et al, 2016b), and (c) equilibrium reverting acceleration (ERA) representing position, velocity, and acceleration (Gan et al, 2019). These three models have similar mean reverting behavior, that is, the state will revert to the mean term μ i , for example set as the destination position for MRD and with (nearly) zero velocity and acceleration for ERV and ERA, respectively.…”

“…Such approaches have been extensively adopted in financial modeling to describe the discrete movements (Kou 2002), and in object tracking field to capture sudden maneuvers undertaken by the target or induced by external forces (Godsill, 2007). Owing to the clear physical representation and computation tractability, such jump diffusion dynamical models have also been employed in Ahmad et al (2014) and Gan et al (2019) within a predictive touch system under high levels of perturbations due to road-driving conditions. The approach presented in Ahmad et al (2014) embedded a self-decay jump process within a Gaussian process to pre-process the highly-perturbed pointing data, with the aim to obtain a smoothed trajectory for the later intent inference task, whereas Gan et al (2019) introduces a jump diffusion model for a unified scheme for destination and state estimation.…”

“…Owing to the clear physical representation and computation tractability, such jump diffusion dynamical models have also been employed in Ahmad et al (2014) and Gan et al (2019) within a predictive touch system under high levels of perturbations due to road-driving conditions. The approach presented in Ahmad et al (2014) embedded a self-decay jump process within a Gaussian process to pre-process the highly-perturbed pointing data, with the aim to obtain a smoothed trajectory for the later intent inference task, whereas Gan et al (2019) introduces a jump diffusion model for a unified scheme for destination and state estimation. In this paper, we mainly discuss the latter recent work given its improved performance.…”

“…The intent inference performance for highly perturbed trajectories in Dataset B has been tested with jump models and Gaussian mean reverting models in Gan et al (2019); Ahmad et al (2016a). Results from the BD models introduced in this paper are also included for comparison.…”