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Computational Intelligence and Neuroscience
Volume 2014, Article ID 761047, 8 pages
Research Article

New Algorithms for Computing the Time-to-Collision in Freeway Traffic Simulation Models

1School of Transportation, Southeast University, No. 2 Sipailou, Nanjing 210096, China
2Department of Civil, Construction, and Environmental Engineering, NCSU, Raleigh, NC 27695, USA

Received 3 October 2014; Accepted 4 December 2014; Published 31 December 2014

Academic Editor: Yongjun Shen

Copyright © 2014 Jia Hou et al. 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.

Citations to this Article [10 citations]

The following is the list of published articles that have cited the current article.

  • Dimia Iberraken, Lounis Adouane, and Dieumet Denis, “Reliable Risk Management for Autonomous Vehicles based on Sequential Bayesian Decision Networks and Dynamic Inter-Vehicular Assessment,” 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 2344–2351, . View at Publisher · View at Google Scholar
  • Dewen Kong, George F. List, Xiucheng Guo, and Dingxin Wu, “Modeling vehicle car-following behavior in congested traffic conditions based on different vehicle combinations,” Transportation Letters, pp. 1–14, 2016. View at Publisher · View at Google Scholar
  • Matevz Bosnak, and Igor Skrjanc, “Efficient time-to-collision estimation for a braking supervision system with LIDAR,” 2017 3rd IEEE International Conference on Cybernetics, CYBCONF 2017 - Proceedings, 2017. View at Publisher · View at Google Scholar
  • Wen-Yuan Qi, Chao Qu, and Ping Wu, “A High Precision and Efficient Time-To-Collision Algorithm for Collision Warning Based V2X Applications,” 2018 2nd International Conference on Robotics and Automation Sciences, ICRAS 2018, pp. 62–69, 2018. View at Publisher · View at Google Scholar
  • P. Sathiya, and P. Anandhakumar, “Probabilistic collision estimation for tracked vehicles based on corner point self-activation approach,” Computers & Electrical Engineering, 2018. View at Publisher · View at Google Scholar
  • Lounis Adouane, Dimia Iberraken, and Dieumet Denis, “Multi-Level Bayesian Decision-Making for Safe and Flexible Autonomous Navigation in Highway Environment,” IEEE International Conference on Intelligent Robots and Systems, pp. 3984–3990, 2018. View at Publisher · View at Google Scholar
  • DImia Iberraken, DIeumet Denis, and Lounis Adouane, “Safe Autonomous Overtaking Maneuver based on Inter-Vehicular Distance Prediction and Multi-Level Bayesian Decision-Making,” IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, vol. 2018-, pp. 3259–3265, 2018. View at Publisher · View at Google Scholar
  • Zoran Duric, Chaitanya Yavvari, and Duminda Wijesekera, “Cooperative Collision Avoidance by Sharing Vehicular Subsystem Data,” IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2018-, pp. 1346–1353, 2018. View at Publisher · View at Google Scholar
  • Mehmet Baran Ulak, Eren Erman Ozguven, Ren Moses, Thobias Sando, Walter Boot, Yassir AbdelRazig, and John Olusegun Sobanjo, “Assessment of traffic performance measures and safety based on driver age and experience: a microsimulation based analysis for an unsignalized T-intersection,” Journal of Traffic and Transportation Engineering (English Edition), 2019. View at Publisher · View at Google Scholar
  • Nadhir Mansour Ben Lakhal, Lounis Adouane, Othman Nasri, and Jaleleddine Ben Hadj Slama, “Risk Management for Intelligent Vehicles based on Interval Analysis of TTC,” IFAC-PapersOnLine, vol. 52, no. 8, pp. 338–343, 2019. View at Publisher · View at Google Scholar