Browse > Article
http://dx.doi.org/10.3795/KSME-A.2013.37.5.609

Design of Near-Minimum Time Path Planning Algorithm for Autonomous Driving  

Kim, Dongwook (School of Mechanical and Aerospace Engineering, Seoul Nat'l Univ.)
Kim, Hakgu (School of Mechanical and Aerospace Engineering, Seoul Nat'l Univ.)
Yi, Kyongsu (School of Mechanical and Aerospace Engineering, Seoul Nat'l Univ.)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.37, no.5, 2013 , pp. 609-617 More about this Journal
Abstract
This paper presents a near-minimum time path planning algorithm for autonomous driving. The problem of near-minimum time path planning is an optimization problem in which it is necessary to take into account not only the geometry of the circuit but also the dynamics of the vehicle. The path planning algorithm consists of a candidate path generation and a velocity optimization algorithm. The candidate path generation algorithm calculates the compromises between the shortest path and the path that allows the highest speeds to be achieved. The velocity optimization algorithm calculates the lap time of each candidate considering the vehicle driving performance and tire friction limit. By using the calculated path and velocity of each candidate, we calculate the lap times and search for a near-minimum time path. The proposed algorithm was evaluated via computer simulation using CarSim and Matlab/Simulink.
Keywords
Near-Minimum Time Path Planning; Path Tracking; Autonomous Driving; Path Optimization; Optimal Preview Control; Model Free Control;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Thrun, S., Montemerlo, M. and Dahlkamp, H., 2006, "Stanley, the Robot that Won the DARPA Grand Challenge," Journal of Field Robotics, Vol. 23, pp. 661-692..   DOI   ScienceOn
2 Gustafsson, F., 1997, "Slip-Based Tire-Road Friction Estimation," Automatica, Vol. 33, pp. 1087-1099   DOI   ScienceOn
3 Cerri, P., Soprani, G. and Choi, J., 2011, "Computer Vision at the Hyundai Autonomous Challenge," Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on, pp. 777-783
4 Yamakadoa, M., Takahashia, J. and Saitoa, S., "Improvement in Vehicle Agility and Stability by GVectoring Control," International Journal of Vehicle Mechanics and Mobility, Vol. 48, pp. 231-254
5 Cho, W., Yoon, J. and Yi, K., "Estimation of Tire Forces for Application to Vehicle Stability Control," IEEE Transations on Vehicular Technology, Vol. 59, pp. 638-649
6 Villagra, J. and Milanes, V., 2012, "Path and Speed Planning for Smooth Autonomous Navigation" IEEE Intelligent Vehicles Symposium.
7 Lini,G. and Piazzi, A., 2010, "Time-Optimal Dynamic Path Inversion for an Automatic Guided Vehicle," 49th IEEE Conference on Decision and Control.
8 Braghin, F., Cheli, F., Melzi, S. and Sabbioni, E., 2008, "Race Driver Model," Computers and Structure 86, 1503-1516.   DOI   ScienceOn
9 Kang, J. Y., 2007, Development of the Human Driver Model Based on the Human Factor, MS Thesis, Seoul National University.
10 Kang, J. and Yi, K., 2006, "Development of a Finite Optimal Preview Control-Based Human Driver Steering Model," KSAE
11 Burl, J. B., 1998, Linear Optimal Control, pp.179-226.
12 Kim, H. and Yi, K., 2012, "Design of a Model Reference Cruise Control Algorithm," SAE2012 World Congress