  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
map, 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 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.
