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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bo Tang Khokhar, S. Gupta, R. |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Electr., Comput. & Biomed. Eng., Univ. of Rhode Island, Kingston, RI, USA (Bo Tang) || Dept. of Electr. Eng. & Comput. Sci., Univ. of Central Florida, Orlando, FL, USA (Khokhar, S.) || Honda Res. Inst. USA Inc., Mountain View, CA, USA (Gupta, R.) |
| Abstract | Navigating a car at intersections is one of the most challenging parts of urban driving. Successful navigation needs predicting of intention of other traffic participants at the intersection. Such prediction is an important component for both Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) Systems. In this paper, we present a driver intention prediction model for general intersections. Our model incorporates lane-level maps of an intersection and makes a prediction based on past position and movement of the vehicle. We create a real-world dataset of 375 turning tracks at a variety of intersections. We present turn prediction results based on Hidden Markov Model (HMM), Support Vector Machine (SVM), and Dynamic Bayesian Network (DBN). SVM and DBN models give higher accuracy compared to HMM models. We get over 90% turn prediction accuracy 1.6 seconds before the intersection. Our work advances the state of art in ADAS/AD systems with a turn prediction model for general intersections. |
| Starting Page | 1399 |
| Ending Page | 1404 |
| File Size | 526270 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781467372664 |
| DOI | 10.1109/IVS.2015.7225911 |
| 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 | Hidden Markov models Vehicles Turning Support vector machines Roads Global Positioning System Predictive models |
| Content Type | Text |
| Resource Type | Article |
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