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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Schlechtriemen, J. Wirthmueller, F. Wedel, A. Breuel, G. Kuhnert, K.-D. |
| Copyright Year | 2015 |
| Description | Author affiliation: Daimler AG, Boblingen, Germany (Schlechtriemen, J.; Wedel, A.; Breuel, G.) || Inst. for Real-Time Learning Syst., Univ. of Siegen, Siegen, Germany (Kuhnert, K.-D.) |
| Abstract | Understanding traffic situations in dynamic traffic environments is an essential requirement for autonomous driving. The prediction of the current traffic scene into the future is one of the main problems in this context. In this publication we focus on highway scenarios, where the maneuver space for traffic participants is limited to a small number of possible behavior classes. Even though there are many publications in the field of maneuver prediction, most of them set the focus on the classification problem, whether a certain maneuver is executed or not. We extend approaches which solve the classification problem of lane-change behavior by introducing the novel aspect of estimating a continuous distribution of possible trajectories. Our novel approach uses the probabilities which are assigned by a Random Decision Forest to each of the maneuvers lane following, lane change left and lane change right. Using measured data of a vehicle and the knowledge of the typical lateral movement of vehicles over time taken from realworlddata, we derive a Gaussian Mixture Regression method. For the final result we combine the predicted probability density functions of the regression method and the computed maneuver probabilities using a Mixture of Experts approach. In a large scale experiment on real world data collected on multiple test drives we trained and validated our prediction model and show the gained high prediction accuracy of the proposed method. |
| Starting Page | 1373 |
| Ending Page | 1379 |
| File Size | 1470827 |
| Page Count | 7 |
| File Format | |
| e-ISBN | 9781467372664 |
| DOI | 10.1109/IVS.2015.7225907 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-06-28 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Vehicles Trajectory Probabilistic logic Prediction algorithms Training Context Vegetation |
| Content Type | Text |
| Resource Type | Article |
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