Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps
© 2018 IEEE. State-of-the-art autonomous driving systems rely heavily on detailed and highly accurate prior maps. However, outside of small urban areas, it is very challenging to build, store, and transmit detailed maps since the spatial scales are so large. Furthermore, maintaining detailed maps of...
| Autores principales: | , , |
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| Otros Autores: | |
| Formato: | Artículo |
| Lenguaje: | English |
| Publicado: |
IEEE
2021
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| Acceso en línea: | https://hdl.handle.net/1721.1/137200 |
| _version_ | 1826211540167032832 |
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| author | Ort, Teddy Paull, Liam Rus, Daniela |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Ort, Teddy Paull, Liam Rus, Daniela |
| author_sort | Ort, Teddy |
| collection | MIT |
| description | © 2018 IEEE. State-of-the-art autonomous driving systems rely heavily on detailed and highly accurate prior maps. However, outside of small urban areas, it is very challenging to build, store, and transmit detailed maps since the spatial scales are so large. Furthermore, maintaining detailed maps of large rural areas can be impracticable due to the rapid rate at which these environments can change. This is a significant limitation for the widespread applicability of autonomous driving technology, which has the potential for an incredibly positive societal impact. In this paper, we address the problem of autonomous navigation in rural environments through a novel mapless driving framework that combines sparse topological maps for global navigation with a sensor-based perception system for local navigation. First, a local navigation goal within the sensor view of the vehicle is chosen as a waypoint leading towards the global goal. Next, the local perception system generates a feasible trajectory in the vehicle frame to reach the waypoint while abiding by the rules of the road for the segment being traversed. These trajectories are updated to remain in the local frame using the vehicle's odometry and the associated uncertainty based on the least-squares residual and a recursive filtering approach, which allows the vehicle to navigate road networks reliably, and at high speed, without detailed prior maps. We demonstrate the performance of the system on a full-scale autonomous vehicle navigating in a challenging rural environment and benchmark the system on a large amount of collected data. |
| first_indexed | 2024-09-23T15:07:38Z |
| format | Article |
| id | mit-1721.1/137200 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:07:38Z |
| publishDate | 2021 |
| publisher | IEEE |
| record_format | dspace |
| spelling | mit-1721.1/1372002023-02-10T21:25:40Z Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps Ort, Teddy Paull, Liam Rus, Daniela Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 2018 IEEE. State-of-the-art autonomous driving systems rely heavily on detailed and highly accurate prior maps. However, outside of small urban areas, it is very challenging to build, store, and transmit detailed maps since the spatial scales are so large. Furthermore, maintaining detailed maps of large rural areas can be impracticable due to the rapid rate at which these environments can change. This is a significant limitation for the widespread applicability of autonomous driving technology, which has the potential for an incredibly positive societal impact. In this paper, we address the problem of autonomous navigation in rural environments through a novel mapless driving framework that combines sparse topological maps for global navigation with a sensor-based perception system for local navigation. First, a local navigation goal within the sensor view of the vehicle is chosen as a waypoint leading towards the global goal. Next, the local perception system generates a feasible trajectory in the vehicle frame to reach the waypoint while abiding by the rules of the road for the segment being traversed. These trajectories are updated to remain in the local frame using the vehicle's odometry and the associated uncertainty based on the least-squares residual and a recursive filtering approach, which allows the vehicle to navigate road networks reliably, and at high speed, without detailed prior maps. We demonstrate the performance of the system on a full-scale autonomous vehicle navigating in a challenging rural environment and benchmark the system on a large amount of collected data. 2021-11-03T14:37:24Z 2021-11-03T14:37:24Z 2018-05 2019-07-17T14:57:38Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137200 Ort, Teddy, Paull, Liam and Rus, Daniela. 2018. "Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps." en 10.1109/icra.2018.8460519 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IEEE MIT web domain |
| spellingShingle | Ort, Teddy Paull, Liam Rus, Daniela Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps |
| title | Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps |
| title_full | Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps |
| title_fullStr | Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps |
| title_full_unstemmed | Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps |
| title_short | Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps |
| title_sort | autonomous vehicle navigation in rural environments without detailed prior maps |
| url | https://hdl.handle.net/1721.1/137200 |
| work_keys_str_mv | AT ortteddy autonomousvehiclenavigationinruralenvironmentswithoutdetailedpriormaps AT paullliam autonomousvehiclenavigationinruralenvironmentswithoutdetailedpriormaps AT rusdaniela autonomousvehiclenavigationinruralenvironmentswithoutdetailedpriormaps |