ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Co-Pilot Agent for Vehicle/Driver Cooperative and Autonomous Driving

  • Noh, Samyeul (IT Convergence Technology Research Laboratory, ETRI) ;
  • Park, Byungjae (IT Convergence Technology Research Laboratory, ETRI) ;
  • An, Kyounghwan (IT Convergence Technology Research Laboratory, ETRI) ;
  • Koo, Yongbon (IT Convergence Technology Research Laboratory, ETRI) ;
  • Han, Wooyong (IT Convergence Technology Research Laboratory, ETRI)
  • Received : 2014.08.08
  • Accepted : 2015.07.16
  • Published : 2015.10.01
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Abstract

ETRI's Co-Pilot project is aimed at the development of an automated vehicle that cooperates with a driver and interacts with other vehicles on the road while obeying traffic rules without collisions. This paper presents a core block within the Co-Pilot system; the block is named "Co-Pilot agent" and consists of several main modules, such as road map generation, decision-making, and trajectory generation. The road map generation builds road map data to provide enhanced and detailed map data. The decision-making, designed to serve situation assessment and behavior planning, evaluates a collision risk of traffic situations and determines maneuvers to follow a global path as well as to avoid collisions. The trajectory generation generates a trajectory to achieve the given maneuver by the decision-making module. The system is implemented in an open-source robot operating system to provide a reusable, hardware-independent software platform; it is then tested on a closed road with other vehicles in several scenarios similar to real road environments to verify that it works properly for cooperative driving with a driver and automated driving.

Keywords

References

  1. J.D. Rupp and A.G. King, "Autonomous Driving-A Practical Roadmap," SAE Technical Paper 2010-01-2335, 2010.
  2. K. Aldana, "U.S. Department of Transportation Releases Policy on Automated Vehicle Development," National Highway Traffic Safety Administration (NHTSA) New Release, May 2013.
  3. SAE International, "Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems," Technical Report SAE J 3016, Jan. 2014.
  4. C. Urmson et al., "Autonomous Driving in Urban Environments: Boss and the Urban Challenge," J. Field Robot., vol. 25, no. 8, Aug. 2008, pp. 425-466. https://doi.org/10.1002/rob.20255
  5. A. Bacha et al., "Odin: Team VictorTangos Entry in the DARPA Urban Challenge," J. Field Robot., vol. 25, no. 8, Aug. 2008, pp. 467-492. https://doi.org/10.1002/rob.20248
  6. M. Montemerlo et al., "Junior: The Stanford Entry in the Urban Challenge," J. Field Robot., vol. 25, no. 9, Aug. 2008, pp. 569-597. https://doi.org/10.1002/rob.20258
  7. ROS. [Online], http://www.ros.org/
  8. S.Y. Cho and H.K. Lee, "Modified RHKF Filter for Improved DR/GPS Navigation against Uncertain Model Dynamics," ETRI J., vol. 34, no. 3, June 2012, pp. 379-387. https://doi.org/10.4218/etrij.12.0111.0391
  9. M. Enkhtur, S.Y. Cho, and K.-H. Kim, "Modified Unscented Kalman Filter for a Multirate INS/GPS Integrated Navigation System," ETRI J., vol. 35, no. 5, Oct. 2013, pp. 943-946. https://doi.org/10.4218/etrij.13.0212.0540
  10. S. Glaser et al., "Maneuver Based Trajectory Planning for Highly Autonomous Vehicles on Real Road with Traffic and Driver Interaction," IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, Sept. 2010, pp. 589-606. https://doi.org/10.1109/TITS.2010.2046037
  11. B. Patrick et al., "Multi-agent Interactions in Urban Driving," J. Physical Agents, vol. 2, no. 1, Mar. 2008, pp. 15-30.
  12. S. Noh, K. An, and W. Han, "Situation Assessment and Behavior Decision for Vehicle/Driver Cooperative Driving in Highway Environments," in proc. of IEEE Int. Conf. Autom. Sci. Eng., Gothenberg, Sweden, Aug. 24-28, 2015, pp. 626-633.
  13. ISO 22179, Intell. Transp. Syst.-Full Speed Range Adaptive Cruise Contr. (FSRA) Syst.-Performance Requirements and Test Procedures, 2009.
  14. S. Noh, K. An, and W. Han, "High-Level Data Fusion Based Probabilistic Situation Assessment for Highly Automated Driving," IEEE Int. Conf. Intell. Transp. Syst., Las Palmas de Gran Canaria, Spain, Sept. 15-18, 2015, pp. 1587-1594.
  15. Korean Traffic Laws. [Online], http://www.law.go.kr/
  16. S. Noh and W. Han, "Independent Object Based Situation Awareness for Autonomous Driving in On-Road Environment," J. Inst. Contr., Robot. Syst., vol. 21, no. 2, Feb. 2015, pp. 87-94. https://doi.org/10.5302/J.ICROS.2015.14.9002
  17. B. Park, Y.-C. Lee, and W. Han, "Trajectory Generation Method Using Bezier Spiral Curves for High-Speed On-Road Autonomous Vehicles," in proc. of IEEE Int. Conf. Autom. Sci. Eng., Taipei, Taiwan, Aug. 18-22, 2014, pp. 927-932.
  18. K. Yang and S. Sukkarieh, "Anytime Synchronized-Biased-Greedy Rapidly-Exploring Random Tree Path Planning in Two Dimensional Complex Environments," Int. J. Contr., Autom., Syst., vol. 9, no. 4, Aug. 2011, pp. 750-758. https://doi.org/10.1007/s12555-011-0417-7

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