Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving

Multi-object state estimation is a fundamental problem for robotic applications where a robot must interact with other moving objects. Typically, other
objects’ relevant state features are not directly observable, and must instead be
inferred from observations. Particle filtering can perform such inference given
approximate transition and observation models. However, these models are often
unknown a priori, yielding a difficult parameter estimation problem since observations jointly carry transition and observation noise. In this work, we consider
learning maximum-likelihood parameters using particle methods. Recent methods addressing this problem typically differentiate through time in a particle filter,
which requires workarounds to the non-differentiable resampling step, that yield
biased or high variance gradient estimates. By contrast, we exploit Fisher’s identity to obtain a particle-based approximation of the score function (the gradient of
the log likelihood) that yields a low variance estimate while only requiring stepwise differentiation through the transition and observation models. We apply our
method to real data collected from autonomous vehicles (AVs) and show that it
learns better models than existing techniques and is more stable in training, yielding an effective smoother for tracking the trajectories of vehicles around an AV.