Dynamic Lambda-Field: A Counterpart of the Bayesian Occupancy Grid for Risk Assessment in Dynamic Environments
This work addresses risk assessment in dynamic environments for autonomous vehicles, offering a novel representation that improves upon existing methods but is incremental in nature.
The authors tackled the problem of risk assessment for autonomous vehicles by proposing the Dynamic Lambda-Field framework, which overcomes limitations of Bayesian occupancy grids by providing risk estimates independent of tessellation size and with meaningful physical units, validated through quantitative experiments showing convergence speed and real-world suitability.
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.