Year: 2025
Type: conference-paper
Abstract: Optimal planning for autonomous vehicles over long time horizons, where sensor information and multiple objectives form part of the decision-making process, is still in its infancy. The challenges of uncertainty, inherent in sensor information, and the computational complexity of their implementation typically render direct approaches infeasible. We present a practical, close to optimal, approach to the problem of long horizon path planning and goal-seeking for an autonomous mobile platform with sensing capability. Optimal autonomous vehicle planning is notoriously difficult, presenting several significant challenges: handling the uncertainty inherent in the sensor data and in a potentially unknown environment; coping with the ostensibly large data storage requirements; and the computational complexity of looking many epochs ahead. To overcome these problems, our model-based solution leverages a stochastic search methodology to obtain long-term, continuous, trajectories. We demonstrate its capability in handling both uncertainty in sensor measurements as well as multiple, possibly conflicting, objectives.
Authors: Christopher Gilliam, Bisma Amjad, Marek Hilton, Sam Pike, Jordi Barr, Alex Kenyon, Nicola Perree, Marco Martorella, Bill Moran, Beth Jelfs
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Defence and Security Accelerator
OpenAlex ID: W4413087372