Utilizing Priors in Sampling-based Cost Minimization

TL;DR

This paper explores how to incorporate prior knowledge into sampling-based algorithms to improve long-term cost minimization for autonomous vehicles, focusing on trajectory optimization under uncertainty.

Contribution

It introduces a novel method for integrating priors into sampling-based cost minimization, enhancing trajectory planning for autonomous vehicles.

Findings

Improved trajectory optimization performance with prior integration.

Enhanced long-term cost minimization accuracy.

Demonstrated effectiveness in autonomous vehicle scenarios.

Abstract

We consider an autonomous vehicle (AV) agent performing a long-term cost-minimization problem in the elapsed time $T$ over sequences of states $s_{1:T}$ and actions $a_{1:T}$ for some fixed, known (though potentially learned) cost function $C(s_t,a_t)$, approximate system dynamics $P$, and distribution over initial states $d_0$. The goal is to minimize the expected cost-to-go of the driving trajectory $\tau = s_1, a_1, ..., s_T, a_T$ from the initial state.