# An accurate and efficient approach to probabilistic conflict prediction

## TL;DR

This paper introduces a faster conflict prediction method for autonomous vehicle path planning that maintains accuracy by leveraging first-passage time distributions and dimension reduction, enabling real-time applications.

## Contribution

The authors develop a novel, computationally efficient conflict prediction approach using first-passage time analysis and dimension reduction for Gaussian processes in vehicle motion.

## Key findings

- Achieves up to ten times faster predictions without loss of accuracy.
- Demonstrates significant reduction in computation time in simulations.
- Applicable to 2-D stochastic processes with piece-wise linear mean and boundary.

## Abstract

Conflict prediction is a vital component of path planning for autonomous vehicles. Prediction methods must be accurate for reliable navigation, but also computationally efficient to enable online path planning. Efficient prediction methods are especially crucial when testing large sets of candidate trajectories. We present a prediction method that has the same accuracy as existing methods, but up to an order of magnitude faster. This is achieved by rewriting the conflict prediction problem in terms of the first-passage time distribution using a dimension-reduction transform. First-passage time distributions are analytically derived for a subset of Gaussian processes describing vehicle motion. The proposed method is applicable to 2-D stochastic processes where the mean can be approximated by line segments, and the conflict boundary can be approximated by piece-wise straight lines. The proposed method was tested in simulation and compared to two probability flow methods, as well as a recent instantaneous conflict probability method. The results demonstrate a significant decrease of computation time.