Long-Range Road Geometry Estimation Using Moving Vehicles and Roadside Observations
This paper presents an algorithm for estimating the shape of the road ahead of a host vehicle equipped with the following onboard sensors: a camera, a radar, and vehicle internal sensors. The aim is to accurately describe the road geometry up to 200 m ahead in highway scenarios. This purpose is acco...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IEEE Intelligent Transportation Systems Society
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/54476 |
| _version_ | 1848759380794671104 |
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| author | Hammarstrand, L. Fatemi, M. Garcia Fernandez, Angel Svensson, L. |
| author_facet | Hammarstrand, L. Fatemi, M. Garcia Fernandez, Angel Svensson, L. |
| author_sort | Hammarstrand, L. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents an algorithm for estimating the shape of the road ahead of a host vehicle equipped with the following onboard sensors: a camera, a radar, and vehicle internal sensors. The aim is to accurately describe the road geometry up to 200 m ahead in highway scenarios. This purpose is accomplished by deriving a precise clothoid-based road model for which we design a Bayesian fusion framework. Using this framework, the road geometry is estimated using sensor observations on the shape of the lane markings, the heading of leading vehicles, and the position of roadside radar reflectors. The evaluation on sensor data shows that the proposed algorithm is capable of capturing the shape of the road well, even in challenging mountainous highways. |
| first_indexed | 2025-11-14T09:58:58Z |
| format | Journal Article |
| id | curtin-20.500.11937-54476 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:58:58Z |
| publishDate | 2016 |
| publisher | IEEE Intelligent Transportation Systems Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-544762017-11-20T07:22:54Z Long-Range Road Geometry Estimation Using Moving Vehicles and Roadside Observations Hammarstrand, L. Fatemi, M. Garcia Fernandez, Angel Svensson, L. This paper presents an algorithm for estimating the shape of the road ahead of a host vehicle equipped with the following onboard sensors: a camera, a radar, and vehicle internal sensors. The aim is to accurately describe the road geometry up to 200 m ahead in highway scenarios. This purpose is accomplished by deriving a precise clothoid-based road model for which we design a Bayesian fusion framework. Using this framework, the road geometry is estimated using sensor observations on the shape of the lane markings, the heading of leading vehicles, and the position of roadside radar reflectors. The evaluation on sensor data shows that the proposed algorithm is capable of capturing the shape of the road well, even in challenging mountainous highways. 2016 Journal Article http://hdl.handle.net/20.500.11937/54476 10.1109/TITS.2016.2517701 IEEE Intelligent Transportation Systems Society restricted |
| spellingShingle | Hammarstrand, L. Fatemi, M. Garcia Fernandez, Angel Svensson, L. Long-Range Road Geometry Estimation Using Moving Vehicles and Roadside Observations |
| title | Long-Range Road Geometry Estimation Using Moving Vehicles and Roadside Observations |
| title_full | Long-Range Road Geometry Estimation Using Moving Vehicles and Roadside Observations |
| title_fullStr | Long-Range Road Geometry Estimation Using Moving Vehicles and Roadside Observations |
| title_full_unstemmed | Long-Range Road Geometry Estimation Using Moving Vehicles and Roadside Observations |
| title_short | Long-Range Road Geometry Estimation Using Moving Vehicles and Roadside Observations |
| title_sort | long-range road geometry estimation using moving vehicles and roadside observations |
| url | http://hdl.handle.net/20.500.11937/54476 |