Lane Change Intention Recognition for Intelligent Connected Vehicle Using Trajectory Prediction

Author:

Kou Shengjie11111,Jiang Kun11111,Yu Weiguang11111,Yan Ruidong11111,Zhou Weitao11111,Yang Mengmeng11111,Yang Diange11111

Affiliation:

1. State Key Laboratory of Automotive Safety and Energy, Centre for Intelligent Connected Vehicles and Transportation, School of Vehicle and Mobility, Tsinghua Univ., Beijing, China.

Publisher

American Society of Civil Engineers

Reference14 articles.

1. Colyar J. and J. Halkias (2006). "Us highway 80 dataset " Federal Highway Administration (FHWA) vol." Tech no. Rep.

2. Colyar J. and J. Halkias (2007). "US highway 101 dataset." Federal Highway Administration (FHWA) Tech. Rep. FHWA-HRT-07-030.

3. Dogan Ü. J. Edelbrunner and I. Iossifidis (2011). Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior. 2011 IEEE International Conference on Robotics and Biomimetics IEEE.

4. Fitch G. S. Lee S. Klauer J. Hankey J. Sudweeks and T. Dingus (2009). "Analysis of lanechange crashes and near-crashes." US Department of Transportation National Highway Traffic Safety Administration.

5. Identification of visual cues and quantification of drivers' perception of proximity risk to the lead vehicle in carfollowing situations;Kondoh T.;Journal of Mechanical Systems for Transportation and Logistics,2008

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