Dynamic Lambda-Field: A Counterpart of the Bayesian Occupancy Grid for Risk Assessment in Dynamic Environments

TL;DR

This paper introduces the Dynamic Lambda-Field, a novel framework for risk assessment in autonomous vehicle navigation that overcomes the limitations of Bayesian occupancy grids by providing physically meaningful risk measures in dynamic environments.

Contribution

The paper presents the Dynamic Lambda-Field, an innovative approach that enables accurate, scalable risk assessment independent of grid tessellation, suitable for real-world autonomous navigation.

Findings

The framework converges quickly in practical scenarios.

It allows adjustable risk functions based on the environment.

Validated with quantitative experiments in real-world settings.

Abstract

In the context of autonomous vehicles, one of the most crucial tasks is to estimate the risk of the undertaken action. While navigating in complex urban environments, the Bayesian occupancy grid is one of the most popular types of maps, where the information of occupancy is stored as the probability of collision. Although widely used, this kind of representation is not well suited for risk assessment: because of its discrete nature, the probability of collision becomes dependent on the tessellation size. Therefore, risk assessments on Bayesian occupancy grids cannot yield risks with meaningful physical units. In this article, we propose an alternative framework called Dynamic Lambda-Field that is able to assess generic physical risks in dynamic environments without being dependent on the tessellation size. Using our framework, we are able to plan safe trajectories where the risk function can be adjusted depending on the scenario. We validate our approach with quantitative experiments, showing the convergence speed of the grid and that the framework is suitable for real-world scenarios.