A new speed planning method based on predictive curvature calculation for autonomous driving

As the number of vehicles in traffic is increasing day by day, the accident rates and driving effort are significantly raising. For this reason, ensuring safety and driving comfort is becoming more and more important. Driver assistance systems are the most common systems that are adopted for this purpose. With the development of technology, lane tracking support and adaptive cruise control systems are now being sold as standard equipment. More advanced research is being done for fully autonomous driving. One of the most critical parts of autonomous driving is speed profile planning. In this paper, a curvature-based predictive speed planner is designed using a fuzzy logic strategy. In addition to the predictive curvature value, lateral error to the planned path is considered for the final decision. With the help of the proposed predictive nature, which is not included in classical curvature-based planners, the vehicle is able to reduce the speed before cornering. Similarly, it starts increasing the speed when it is still in a curve but very near to the straight part of the road. In order to illustrate the efficiency of the proposed method and compare it with other approaches, the simulations are performed using realistic vehicle dynamics models of CarMaker software. After the comparative analysis, the designed planner is utilized on a real 1/10 scaled autonomous vehicle platform to show its real-world performance. The results show that the proposed speed planner is an effective and promising algorithm for both the comfort and path tracking performance.

PDF

___

[1] Perez-D’Arpino C, Medina-Melendez W, Guzman J, Fermin L, Fernandez-Lopez G. Fuzzy logic based speed planning for autonomous navigation under velocity field control. In: 2009 IEEE International Conference on Mechatronics. IEEE; 2009. pp. 1-6.
[2] González D, Milanés V, Pérez J, Nashashibi F. Speed profile generation based on quintic Bézier curves for enhanced passenger comfort. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC). IEEE; 2016. pp. 814-819.
[3] Demir M, Sezer V. Design and implementation of a new speed planner for semiautonomous systems. Turkish Journal of Electrical Engineering & Computer Sciences. 2018;26 (2):693-706. doi: 10.3906/elk-1708-127
[4] Sezer V. Combined fuzzy approach for online speed planning and control with real vehicle implementation. International Journal of Vehicle Design. 2015;68 (4):329-345. doi: 10.1504/IJVD.2015.071098
[5] Sezer V, Ercan Z, Heceoglu H, Bogosyan S, Gokasan M. A new fuzzy speed planning method for safe navigation. In: 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012). IEEE; 2012. pp. 381-386
[6] Zhang Y, Chen H, Waslander SL, Yang T, Zhang S et al. Toward a more complete, flexible, and safer speed planning for autonomous driving via convex optimization. Sensors. 2018;18 (7):2185. doi: 10.3390/s18072185
[7] Zhang B, Cao W, Shen T. Two-stage on-board optimization of merging velocity planning with energy management for HEVs. Control Theory and Technology. 2019;17 (4):335-345
[8] Herrmann T, Wischnewski A, Hermansdorfer L, Betz J, Lienkamp M. Real-time adaptive velocity optimization for autonomous electric cars at the limits of handling. IEEE Transactions on Intelligent Vehicles. 2020;6 (4):665-677.
[9] Villagra J, Milanés V, Pérez J, Godoy J. Smooth path and speed planning for an automated public transport vehicle. Robotics and Autonomous Systems. 2012;60 (2):252-265. doi: 10.1016/j.robot.2011.11.001
[10] Consolini L, Locatelli M, Minari A, Piazzi A. A linear-time algorithm for minimum-time velocity planning of autonomous vehicles. In: 2016 24th Mediterranean Conference on Control and Automation (MED). IEEE; 2016. pp. 490-495.
[11] Wang M, Liu Q, Zheng Y. A curvature-segmentation-based minimum time algorithm for autonomous vehicle velocity planning. Information Sciences. 2021;565:248-261.
[12] Nagel J, Trepagnier PG, Koutsougeras C, Kinney PM, Dooner M. The Culebra algorithm for path planning and obstacle avoidance in Kat-5. In: 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’06). IEEE; 2006. pp. 247-253.
[13] Cui Z, Guo X, Pei X. A Novel Velocity Planner for Autonomous Vehicle Considering Human Driver’s Habits. SAE Technical Paper, 2020.
[14] Zhang D, Xiao Q, Wang J, Li K. Driver curve speed model and its application to ACC speed control in curved roads. International journal of automotive technology. 2013;14 (2):241-247. doi: 10.1007/s12239-013-0027-x
[15] Gillespie TD. Fundamentals of vehicle dynamics. SAE Technical Paper, 1992.
[16] Coulter RC. Implementation of the pure pursuit path tracking algorithm. Carnegie-Mellon UNIV Pittsburgh PA Robotics INST, 1992.
[17] Hoffmann GM, Tomlin CJ, Montemerlo M, Thrun S. Autonomous automobile trajectory tracking for off-road driving: Controller design, experimental validation and racing. In: 2007 American Control Conference. IEEE; 2007. pp. 2296-2301.
[18] Ahn J, Shin S, Kim M, Park J. Accurate path tracking by adjusting look-ahead point in pure pursuit method. International journal of automotive technology. 2021;22 (1):119-129. doi: 10.1007/s12239-021-0013-7
[19] Sadollah A. Introductory Chapter: Which Membership Function is Appropriate in Fuzzy System? In: Sadollah A, editor. Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications. Rijeka: IntechOpen; 2018. doi:10.5772/intechopen.79552
[20] Gilda K, Satarkar S. Review of fuzzy systems through various jargons of technology. Int J Emerg Technologies Innov Res. 2020;7:260-264.
[21] Teodorović D, Janić M. Chapter 11 - Transportation, Environment, and Society. In: Teodorović D, Janić M, editors. Transportation Engineering. Butterworth-Heinemann 2017; 719-858.
[22] Ni M. Study on Mechanical Effect of the Vehicle at Corners. In: 2015 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering. Atlantis Press; 2015; 614-617.
[23] Singh KB, Taheri S. Estimation of tire–road friction coefficient and its application in chassis control systems. Systems Science & Control Engineering. 2015;3 (1):39-61.
[24] Wang JY, Lin YB. Game ai: Simulating car racing game by applying pathfinding algorithms. International Journal of Machine Learning and Computing. 2012;2 (1):13. doi: 10.7763/IJMLC.2012.V2.82
[25] ISO I. 2631-1: Mechanical vibration and shock-evaluation of human exposure to whole-body vibration-Part 1: General requirements. Geneva, Switzerland: ISO. 1997.
[26] Murray JW. C# game programming cookbook for Unity 3D. CRC Press, 2021.
[27] Abdelrasoul Y, Saman ABSH, Sebastian P. A quantitative study of tuning ROS gmapping parameters and their effect on performing indoor 2D SLAM. In: 2016 2nd IEEE international symposium on robotics and manufacturing automation. IEEE; 2016. pp. 1-6.
[28] Quigley M, Conley K, Gerkey B, Faust J, Foote T et al. ROS: an open-source Robot Operating System. In: ICRA workshop on open source software. vol. 3. Kobe, Japan; 2009. pp. 5.
[29] Zou Q, Sun Q, Chen L, Nie B, Li Q. A Comparative Analysis of LiDAR SLAM-Based Indoor Navigation for Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems. 2021:1-15. doi: 10.1109/TITS.2021.3063477
[30] Warner J, Sexauer J,Fuzzy S, twmeggs, alexsavio, Unnikrishnan A et al. JDWarner/scikit-fuzzy: Scikit-Fuzzy version 0.4.2. Zenodo; 2019.