Lane Keeping Assistance with Learning-Based Driver Model and Model Predictive Control
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20149326
- Paper/Info type
- AVEC
No.ThC3-2
- Pages
- 1-8(Total 8 p)
- Date of publication
- Sep 2014
- Publisher
- Others, Unknown
- Language
- English
- Event
- AVEC '14
Detailed Information
| Category(E) | ThC3: Testing Method and Evaluation |
|---|---|
| Author(E) | 1) Stéphanie Lefèvre, 2) Yiqi Gao, 3) Dizan Vasquez, 4) H. Eric Tseng, 5) Ruzena Bajcsy, 6) Francesco Borrelli |
| Affiliation(E) | 1) University of California, Berkeley/Inria, 2) University of California, Berkeley, 3) Inria, 4) Ford Research Laboratories, 5) University of California, Berkeley, 6) University of California, Berkeley |
| Abstract(J) | 学習ベースのドライバモデルを用いたアクティブレーンキープ支援システムを検討した．35分の高速道路運転の実際のデータから得られた結果で，隠れマルコフモデルとガウス混合回帰を組み合わせたドライバモデルによって，レーン逸脱を予測できることがことがわかった．レーン逸脱の予測があるときはコントローラが車両をレーンに保持する． Translation |
| Abstract(E) | This paper proposes a novel active Lane Keeping Assistance Systems (LKAS) which relies on a learning-based driver model. The driver model detects unintentional lane departures earlier than existing LKAS, and as a result the correction needed to keep the vehicle in the lane is smaller. When the controller has control of the car, the driver model estimates what the driver would do to keep the car in the lane, and the controller tries to reproduce that behavior as much as possible so that the controlled motion feels comfortable for the driver. The driver model combines a Hidden Markov Model and Gaussian Mixture Regression. The controller is a Nonlinear Model Predictive Controller. The results obtained with real data show that our driver model can reliably predict lane departures. The controller is able to keep the car in the lane when there is a risk of lane departure, and does so less intrusively than existing LKAS. |
