Abstract: This document describes techniques, apparatuses, and systems for a grid-based road model with multiple layers. An example road-perception system generates a roadway grid representation that includes multiple cells. The road-perception system uses data from multiple information sources to generate a road model that includes multiple layers. Each layer represents a roadway attribute of each cell in the grid and includes one or more layer hypotheses. In this way, the described techniques and systems can provide an accurate and reliable road model and quantify uncertainty therein.

Abstract: This document describes techniques and systems for a road-perception system to estimate a belief and plausibility of lanes in a road model. The road-perception system models lanes of a road using basis bands to represent lane sections. The basis bands comprise points representing a polyline with a width at each point. The use of basis bands results in lower-computational requirements and a more stable road model than grid-based representations of a driving environment. The road-perception system also determines a belief mass associated with the lane sections. The road-perception system then computes, using the belief masses, a belief parameter and a plausibility parameter associated with proposed lanes of the road model. In this way, the described techniques and systems can provide an accurate and reliable road model and quantify uncertainty therein.

Abstract: This document describes techniques, apparatuses, and systems for a grid-based road model with multiple layers. An example road-perception system generates a roadway grid representation that includes multiple cells. The road-perception system uses data from multiple information sources to generate a road model that includes multiple layers. Each layer represents a roadway attribute of each cell in the grid and includes one or more layer hypotheses. In this way, the described techniques and systems can provide an accurate and reliable road model and quantify uncertainty therein.

Abstract: This document describes techniques, apparatuses, and systems for a grid-based road model with multiple layers. An example road-perception system generates a roadway grid representation that includes multiple cells. The road-perception system uses data from multiple information sources to generate a road model that includes multiple layers. Each layer represents a roadway attribute of each cell in the grid and includes one or more layer hypotheses. In this way, the described techniques and systems can provide an accurate and reliable road model and quantify uncertainty therein.

Abstract: This document describes techniques, apparatuses, and systems for a grid-based road model with multiple layers. An example road-perception system generates a roadway grid representation that includes multiple cells. The road-perception system uses data from multiple information sources to generate a road model that includes multiple layers. Each layer represents a roadway attribute of each cell in the grid and includes one or more layer hypotheses. In this way, the described techniques and systems can provide an accurate and reliable road model and quantify uncertainty therein.

Abstract: This document describes techniques and systems for a road-perception system to estimate a belief and plausibility of lanes in a road model. The road-perception system models lanes of a road using basis bands to represent lane sections. The basis bands comprise points representing a polyline with a width at each point. The use of basis bands results in lower-computational requirements and a more stable road model than grid-based representations of a driving environment. The road-perception system also determines a belief mass associated with the lane sections. The road-perception system then computes, using the belief masses, a belief parameter and a plausibility parameter associated with proposed lanes of the road model. In this way, the described techniques and systems can provide an accurate and reliable road model and quantify uncertainty therein.