Probabilistic lane estimation for autonomous driving using basis curves
Lane estimation for autonomous driving can be formulated as a curve estimation problem, where local sensor data provides partial and noisy observations of spatial curves forming lane boundaries. The number of lanes to estimate are initially unknown and many observations may be outliers or false dete...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Springer-Verlag
2012
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| Online Access: | http://hdl.handle.net/1721.1/73539 |
| _version_ | 1811075506678792192 |
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| author | Huang, Albert S. Teller, Seth |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Huang, Albert S. Teller, Seth |
| author_sort | Huang, Albert S. |
| collection | MIT |
| description | Lane estimation for autonomous driving can be formulated as a curve estimation problem, where local sensor data provides partial and noisy observations of spatial curves forming lane boundaries. The number of lanes to estimate are initially unknown and many observations may be outliers or false detections (due e.g. to shadows or non-boundary road paint). The challenges lie in detecting lanes when and where they exist, and updating lane estimates as new observations are made.
This paper describes an efficient probabilistic lane estimation algorithm based on a novel curve representation. The key advance is a principled mechanism to describe many similar curves as variations of a single basis curve. Locally observed road paint and curb features are then fused to detect and estimate all nearby travel lanes. The system handles roads with complex multi-lane geometries and makes no assumptions about the position and orientation of the vehicle with respect to the roadway.
We evaluate our algorithm using a ground truth dataset containing manually-labeled, fine-grained lane geometries for vehicle travel in two large and diverse datasets that include more than 300,000 images and 44 km of roadway. The results illustrate the capabilities of our algorithm for robust lane estimation in the face of challenging conditions and unknown roadways. |
| first_indexed | 2024-09-23T10:07:12Z |
| format | Article |
| id | mit-1721.1/73539 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:07:12Z |
| publishDate | 2012 |
| publisher | Springer-Verlag |
| record_format | dspace |
| spelling | mit-1721.1/735392022-09-30T19:01:02Z Probabilistic lane estimation for autonomous driving using basis curves Huang, Albert S. Teller, Seth Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Huang, Albert S. Teller, Seth Lane estimation for autonomous driving can be formulated as a curve estimation problem, where local sensor data provides partial and noisy observations of spatial curves forming lane boundaries. The number of lanes to estimate are initially unknown and many observations may be outliers or false detections (due e.g. to shadows or non-boundary road paint). The challenges lie in detecting lanes when and where they exist, and updating lane estimates as new observations are made. This paper describes an efficient probabilistic lane estimation algorithm based on a novel curve representation. The key advance is a principled mechanism to describe many similar curves as variations of a single basis curve. Locally observed road paint and curb features are then fused to detect and estimate all nearby travel lanes. The system handles roads with complex multi-lane geometries and makes no assumptions about the position and orientation of the vehicle with respect to the roadway. We evaluate our algorithm using a ground truth dataset containing manually-labeled, fine-grained lane geometries for vehicle travel in two large and diverse datasets that include more than 300,000 images and 44 km of roadway. The results illustrate the capabilities of our algorithm for robust lane estimation in the face of challenging conditions and unknown roadways. United States. Defense Advanced Research Projects Agency (Urban Challenge, ARPA Order No. W369/00, Program Code DIRO, issued by DARPA/CMO under Contract No. HR0011-06-C-0149) 2012-10-02T14:03:45Z 2012-10-02T14:03:45Z 2011-09 2010-11 Article http://purl.org/eprint/type/JournalArticle 0929-5593 1573-7527 http://hdl.handle.net/1721.1/73539 Huang, Albert S., and Seth Teller. “Probabilistic Lane Estimation for Autonomous Driving Using Basis Curves.” Autonomous Robots 31.2-3 (2011): 269–283. en_US http://dx.doi.org/10.1007/s10514-011-9251-2 Autonomous Robots Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Springer-Verlag MIT web domain |
| spellingShingle | Huang, Albert S. Teller, Seth Probabilistic lane estimation for autonomous driving using basis curves |
| title | Probabilistic lane estimation for autonomous driving using basis curves |
| title_full | Probabilistic lane estimation for autonomous driving using basis curves |
| title_fullStr | Probabilistic lane estimation for autonomous driving using basis curves |
| title_full_unstemmed | Probabilistic lane estimation for autonomous driving using basis curves |
| title_short | Probabilistic lane estimation for autonomous driving using basis curves |
| title_sort | probabilistic lane estimation for autonomous driving using basis curves |
| url | http://hdl.handle.net/1721.1/73539 |
| work_keys_str_mv | AT huangalberts probabilisticlaneestimationforautonomousdrivingusingbasiscurves AT tellerseth probabilisticlaneestimationforautonomousdrivingusingbasiscurves |