An Adaptive Rear-End Collision Warning System for Drivers That Estimates Driving Phase and Selects Training Data
The paper proposes a rear-end collision warning system for drivers, where the collision risk is adaptively set from driving signals. The system employs the inverse of the time-to-collision with a constant relative acceleration as the risk and the one-class support vector machine as the anomaly detec...
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| Veröffentlicht in: | Shisutemu Seigyo Jouhou Gakkai rombunshi Control and Information Engineers, 2011, Vol.24(8), pp.193-199 |
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| container_title | Shisutemu Seigyo Jouhou Gakkai rombunshi |
| container_volume | 24 |
| creator | Ikeda, Kazushi Mima, Hiroki Inoue, Yuta Shibata, Tomohiro Fukaya, Naoki Hitomi, Kentaro Bando, Takashi |
| description | The paper proposes a rear-end collision warning system for drivers, where the collision risk is adaptively set from driving signals. The system employs the inverse of the time-to-collision with a constant relative acceleration as the risk and the one-class support vector machine as the anomaly detector. The system also utilizes brake sequences for outliers detection. When a brake sequence has a low likelihood with respect to trained hidden Markov models, the driving data during the sequence are removed from the training dataset. This data selection is confirmed to increase the robustness of the system by computer simulations. |
| doi_str_mv | 10.5687/iscie.24.193 |
| format | Article |
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| identifier | ISSN: 1342-5668 |
| ispartof | Transactions of the Institute of Systems, Control and Information Engineers, 2011, Vol.24(8), pp.193-199 |
| issn | 1342-5668 2185-811X |
| language | eng |
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| source | J-STAGE Free; EZB-FREE-00999 freely available EZB journals |
| subjects | advanced driver assistance system hidden Markov model machine learning risk index support vector machine |
| title | An Adaptive Rear-End Collision Warning System for Drivers That Estimates Driving Phase and Selects Training Data |
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