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Appl. Sci. 2017, 7(4), 426; doi:10.3390/app7040426

A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

1
Department of Automation, University of Science and Technology of China, Hefei 230026, China
2
Institute of Applied Technology, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230008, China
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 19 January 2017 / Revised: 3 April 2017 / Accepted: 18 April 2017 / Published: 22 April 2017
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Abstract

Driving through dynamically changing traffic scenarios is a highly challenging task for autonomous vehicles, especially on urban roadways. Prediction of surrounding vehicles’ driving behaviors plays a crucial role in autonomous vehicles. Most traditional driving behavior prediction models work only for a specific traffic scenario and cannot be adapted to different scenarios. In addition, priori driving knowledge was never considered sufficiently. This study proposes a novel scenario-adaptive approach to solve these problems. A novel ontology model was developed to model traffic scenarios. Continuous features of driving behavior were learned by Hidden Markov Models (HMMs). Then, a knowledge base was constructed to specify the model adaptation strategies and store priori probabilities based on the scenario’s characteristics. Finally, the target vehicle’s future behavior was predicted considering both a posteriori probabilities and a priori probabilities. The proposed approach was sufficiently evaluated with a real autonomous vehicle. The application scope of traditional models can be extended to a variety of scenarios, while the prediction performance can be improved by the consideration of priori knowledge. For lane-changing behaviors, the prediction time horizon can be extended by up to 56% (0.76 s) on average. Meanwhile, long-term prediction precision can be enhanced by over 26%. View Full-Text
Keywords: autonomous vehicle; scenario-adaptive; driving behavior prediction; ontology model autonomous vehicle; scenario-adaptive; driving behavior prediction; ontology model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Geng, X.; Liang, H.; Yu, B.; Zhao, P.; He, L.; Huang, R. A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving. Appl. Sci. 2017, 7, 426.

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