Integrating occlusion awareness in urban motion prediction for enhanced autonomous vehicle navigation

TL;DR

This paper introduces the MultIAMP framework, enhanced to account for occlusions in urban environments, improving the safety and reliability of autonomous vehicle motion prediction in complex scenarios.

Contribution

It extends the MultIAMP framework to incorporate occlusion awareness, a relatively unexplored area, using a Dynamic Bayesian Network and Markov chains.

Findings

Improved motion prediction accuracy in occluded scenarios

Enhanced safety in complex urban navigation

Effective integration with state-of-the-art motion planners

Abstract

Motion prediction is a key factor towards the full deployment of autonomous vehicles. It is fundamental in order to ensure safety while navigating through highly interactive and complex scenarios. Lack of visibility due to an obstructed view or sensor range poses a great safety issue for autonomous vehicles. The inclusion of occlusion in interaction-aware approaches is not very well explored in the literature. In this work, the MultIAMP framework, which produces multimodal probabilistic outputs from the integration of a Dynamic Bayesian Network and Markov chains, is extended to tackle occlusions. The framework is evaluated with a state-of-the-art motion planner in two realistic use cases.