Evidential Semantic Lane-Grid for Unknown Space Analysis and High-Level Representation of Dynamic Occupancy Grids

Dynamic Occupancy Grids are a popular approach for the estimation of the environment in autonomous driving applications. They offer several advantages, such as the capability to precisely address any type of obstacle and the estimation of the free and unknown space. Nevertheless, they lack contextual information and rely on a low-level representation format. Moreover, additional features beyond occupancy and dynamic states are required to achieve a comprehensive situation understanding. This work proposes an Evidential Semantic Lane-grid as a complementary environment representation to tackle these issues. First, the environment description of the Dynamic Occupancy Grid is extended by analyzing various factors that can influence the perception system's ability to estimate the surroundings and lead to model the unknown space. Then, this enhanced environment description is converted into a high-level representation format based on a semantic lane-grid. This format incorporates the context of the road layout and synthesizes the data with semantic labels, which are consistently estimated over time using an evidential filtering method that supports their meaning and prioritization criteria. Experimental results in real-world urban scenarios show the utility and feasibility of the proposed approach.