AI Article Synopsis
- The paper discusses an occupancy grid model used for autonomous vehicle navigation, emphasizing the need for a semantic understanding of road spaces, which goes beyond simple obstacle detection.
- It introduces a grid-based evidential approach that utilizes prior lane-level maps to incorporate road rules and manage localization uncertainty, ensuring better navigation within complex driving environments.
- The approach combines data from an external sensor and utilizes Dempster-Shafer theory for an evidential framework, showcasing its effectiveness through real-world tests and providing both qualitative and quantitative results of the method's performance.
Article Abstract
Occupancy grid is a popular environment model that is widely applied for autonomous navigation of mobile robots. This model encodes obstacle information into the grid cells as a reference of the space state. However, when navigating on roads, the planning module of an autonomous vehicle needs to have semantic understanding of the scene, especially concerning the accessibility of the driving space. This paper presents a grid-based evidential approach for modeling semantic road space by taking advantage of a prior map that contains lane-level information. Road rules are encoded in the grid for semantic understanding. Our approach focuses on dealing with the localization uncertainty, which is a key issue, while parsing information from the prior map. Readings from an exteroceptive sensor are as well integrated in the grid to provide real-time obstacle information. All the information is managed in an evidential framework based on Dempster-Shafer theory. Real road results are reported with qualitative evaluation and quantitative analysis of the constructed grids to show the performance and the behavior of the method for real-time application.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7013605 | PMC |
| http://dx.doi.org/10.3390/s20020352 | DOI Listing |
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