Parameter Adjustments in POMDP-Based Trajectory Planning for Unsignalized Intersections

TL;DR

This paper presents a POMDP-based trajectory planning method for autonomous vehicles at unsignalized intersections, demonstrating collision-free navigation through simulations and analyzing parameter effects on performance.

Contribution

Introduces a POMDP framework with ABT algorithm for safe trajectory planning at intersections, including parameter adjustment analysis for improved performance.

Findings

Method achieves collision-free trajectories in simulations

Parameter tuning significantly affects planning efficiency

Provides guidelines for setting ABT parameters

Abstract

This paper investigates the problem of trajectory planning for autonomous vehicles at unsignalized intersections, specifically focusing on scenarios where the vehicle lacks the right of way and yet must cross safely. To address this issue, we have employed a method based on the Partially Observable Markov Decision Processes (POMDPs) framework designed for planning under uncertainty. The method utilizes the Adaptive Belief Tree (ABT) algorithm as an approximate solver for the POMDPs. We outline the POMDP formulation, beginning with discretizing the intersection's topology. Additionally, we present a dynamics model for the prediction of the evolving states of vehicles, such as their position and velocity. Using an observation model, we also describe the connection of those states with the imperfect (noisy) available measurements. Our results confirmed that the method is able to plan collision-free trajectories in a series of simulations utilizing real-world traffic data from aerial footage of two distinct intersections. Furthermore, we studied the impact of parameter adjustments of the ABT algorithm on the method's performance. This provides guidance in determining reasonable parameter settings, which is valuable for future method applications.