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All issues Volume 355 (2022) MATEC Web Conf., 355 (2022) 03031 References
Open Access
Issue
MATEC Web Conf.
Volume 355, 2022
2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
Article Number 03031
Number of page(s) 7
Section Computing Methods and Computer Application
DOI https://doi.org/10.1051/matecconf/202235503031
Published online 12 January 2022
  1. Yuan S.Y. (2016) Decision-making Modeling of Lane Changing Behavior for Autonomous Vehicles in Urban Road Environment. Beijing. Beijing Institute of Technology. [Google Scholar]
  2. Song W.L. (2016) Research on behavioral decision making for intelligent vehicles in dynamic urban environments [D]. Beijing. Beijing Institute of Technology. [Google Scholar]
  3. Zhang W.Z. (2014) Research on Automatic Driving System for Unmanned Ground Vehicles Based on Human-Simulated Intelligent Control. Hefei. University of Science and Technology of China. [Google Scholar]
  4. Qin J.H. (2013) The Study on Vehicles’ Motion Controller and Dynamics Model for the Lane-changing. Xi’an. Chang’an University. [Google Scholar]
  5. E W.J. (2012) Research on Vehicle Conflict Detection and Resolution Algorithm on Unsignalized Intersection. Jilin. Jilin University. [Google Scholar]
  6. Ge R.H., Xiao X., Hong L., Cui Z.Y. (2020) Collision Avoidance Strategy of Turning Vehicles to Crossing Pedestrians at Intersections. Automobile Technology, [2020-0928]:1-5. [Google Scholar]
  7. Fu Z.C. (2020) Virtual Test Based on PreScan for Unmanned Ground Vehicle. Handan. Hebei University of Engineering. [Google Scholar]
  8. Hu Y.P., Wang X.J., Xu P., Liu F. (2019) AEB Control for Cross Trajectory Work Conditions Based on PreScan. Machanical Engineering & Automation. 2019(05):29-31. [Google Scholar]
  9. Hu Y.H. (2019) A System Design on Lane Keeping Assistance System Based on PreScan. Changsha. Hunan University. [Google Scholar]
  10. Peng T, Su L, Zhang R, et al. (2020), A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles. Expert Systems with Applications, 141: 112953. [Google Scholar]
  11. Wu Z, Yang J, Huang L. (2013), Study on the collision avoidance strategy at unsignalized intersection based on PreScan simulation. Procedia-social and behavioral sciences, 96: 1315-1321. [Google Scholar]
  12. Ali S. (2017), Towards Human-Like Automated Driving: Learning Spacing Profiles from Human Driving Data. Wayne State University. [Google Scholar]
  13. Gietelink O J, Ploeg J, De Schutter B, et al. (2009), Development of a driver information and warning system with vehicle hardware-in-the-loop simulations. Mechatronics, 19(7): 1091-1104. [Google Scholar]
  14. Li J.X, Yao L., Xu X., Cheng B., Ren J.K. (2020), Deep reinforcement learning for pedestrian collision avoidance and human-machine cooperative driving. Information Sciences, 532. [Google Scholar]
  15. Xiong Z.B., Yang W., Ding K., Liang F.H, Zheng L., Li Y.S. (2017) Research on the Auto Parking Algorithm [Google Scholar]
  16. Based on the Preview Fuzzy Control. Journal of Chongqing University of Technology(Natural Science), 31(02):14-22. [Google Scholar]
  17. Deo N, Trivedi M M. (2018), Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms. In:2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, 1179-1184. [Google Scholar]
  18. Fan W., Kun F., Yang W., et al. (2017), A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects. Algorithms, 10(2):37-46. [Google Scholar]
  19. Han T., Jing J., Ozguner U., et al. (2019), Driving Intention Recognition and Lane Change Prediction on the Highway. In: 2019 30th IEEE Intelligent Vehicles Symposium, Paris, 957-962. [Google Scholar]
  20. Wang H., Raj B. (2015), A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas. Computer ence, 226(1-4):23-34. [Google Scholar]
  21. Wang L.T., Zhang L., Yi Z. (2017), Trajectory Predictor by Using Recurrent Neural Networks in Visual Tracking. Cybernetics IEEE Transactions On, 47(10):3172-3183. [Google Scholar]
  22. Chelbi N E, Gingras D, Sauvageau C. (2018), Proposal of a new virtual evaluation approach of preventive safety applications and advanced driver assistance functions– application: AEB system. IET Intelligent Transport Systems, 12(9): 1148-1156. [Google Scholar]

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