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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nakatsubo, K. Yamada, K. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Information Engineering, Faculty of Science and Technology, Meijo University, Nagoya 480-1192 Japan (Yamada, K.) || Department of Information Engineering, Graduate School of Science and Technology, Meijo University, Nagoya 480-1192 Japan (Nakatsubo, K.) |
| Abstract | Pedestrian protection is an important issue for intelligent vehicles. This paper proposes a new approach for predicting the possibility of a collision between a vehicle and a pedestrian. Almost all pedestrian behavior toward vehicles observed in the real world is considered safe. Therefore, pedestrian behavior that deviates from usual pedestrian behavior indicates a possibility where the vehicle must take urgent evasive action to avoid collision with the pedestrian. From such a viewpoint, this paper proposes a method for predicting whether the pedestrian behavior deviates from usual pedestrian behavior. Usual pedestrian behavior as observed from the vehicle is modeled with machine learning to detect whether a new observed behavior deviates from the model of the usual pedestrian behavior. The effectiveness of the proposed method is demonstrated with an experiment conducted in a simple road environment. |
| Starting Page | 401 |
| Ending Page | 405 |
| File Size | 593164 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424478668 |
| ISSN | 19310587 |
| e-ISBN | 9781424478682 |
| DOI | 10.1109/IVS.2010.5548050 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-21 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Vehicle detection Collision avoidance Intelligent vehicles Vehicle safety Cameras Protection Predictive models Trajectory Remotely operated vehicles Event detection |
| Content Type | Text |
| Resource Type | Article |
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