Bayesian Road Estimation Using Onboard Sensors
This paper describes an algorithm for estimating the road ahead of a host vehicle based on the measurements from several onboard sensors: a camera, a radar, wheel speed sensors,and an inertial measurement unit.We propose a novel road model that is able to describe the road ahead with higher accuracy...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IEEE Intelligent Transportation Systems Society
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/9349 |
| _version_ | 1848745924199710720 |
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| author | Garcia Fernandez, Angel Hammarstrand, L. Fatemi, M. Svensson, L. |
| author_facet | Garcia Fernandez, Angel Hammarstrand, L. Fatemi, M. Svensson, L. |
| author_sort | Garcia Fernandez, Angel |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper describes an algorithm for estimating the road ahead of a host vehicle based on the measurements from several onboard sensors: a camera, a radar, wheel speed sensors,and an inertial measurement unit.We propose a novel road model that is able to describe the road ahead with higher accuracy than the usual polynomial model. We also develop a Bayesian fusionsystem that uses the following information from the surroundings: lane marking measurements obtained by the camera and leading vehicle and stationary object measurements obtained bya radar–camera fusion system. The performance of our fusion algorithm is evaluated in several drive tests. As expected, the more information we use, the better the performance is.Index Terms—Camera, information fusion, radar, road geometry,unscented Kalman filter (UKF). |
| first_indexed | 2025-11-14T06:25:05Z |
| format | Journal Article |
| id | curtin-20.500.11937-9349 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:25:05Z |
| publishDate | 2014 |
| publisher | IEEE Intelligent Transportation Systems Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-93492017-09-13T14:52:05Z Bayesian Road Estimation Using Onboard Sensors Garcia Fernandez, Angel Hammarstrand, L. Fatemi, M. Svensson, L. Camera unscented Kalman filter (UKF) road geometry radar information fusion This paper describes an algorithm for estimating the road ahead of a host vehicle based on the measurements from several onboard sensors: a camera, a radar, wheel speed sensors,and an inertial measurement unit.We propose a novel road model that is able to describe the road ahead with higher accuracy than the usual polynomial model. We also develop a Bayesian fusionsystem that uses the following information from the surroundings: lane marking measurements obtained by the camera and leading vehicle and stationary object measurements obtained bya radar–camera fusion system. The performance of our fusion algorithm is evaluated in several drive tests. As expected, the more information we use, the better the performance is.Index Terms—Camera, information fusion, radar, road geometry,unscented Kalman filter (UKF). 2014 Journal Article http://hdl.handle.net/20.500.11937/9349 10.1109/TITS.2014.2303811 IEEE Intelligent Transportation Systems Society fulltext |
| spellingShingle | Camera unscented Kalman filter (UKF) road geometry radar information fusion Garcia Fernandez, Angel Hammarstrand, L. Fatemi, M. Svensson, L. Bayesian Road Estimation Using Onboard Sensors |
| title | Bayesian Road Estimation Using Onboard Sensors |
| title_full | Bayesian Road Estimation Using Onboard Sensors |
| title_fullStr | Bayesian Road Estimation Using Onboard Sensors |
| title_full_unstemmed | Bayesian Road Estimation Using Onboard Sensors |
| title_short | Bayesian Road Estimation Using Onboard Sensors |
| title_sort | bayesian road estimation using onboard sensors |
| topic | Camera unscented Kalman filter (UKF) road geometry radar information fusion |
| url | http://hdl.handle.net/20.500.11937/9349 |