A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
AbstractDriving 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
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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.
Geng X, Liang H, Yu B, Zhao P, He L, Huang R. A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving. Applied Sciences. 2017; 7(4):426.Chicago/Turabian Style
Geng, Xinli; Liang, Huawei; Yu, Biao; Zhao, Pan; He, Liuwei; Huang, Rulin. 2017. "A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving." Appl. Sci. 7, no. 4: 426.
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