Date Log
Submitted
Jul 20, 2020
Published
Mar 27, 2019
OVERALL EVALUATION MOTION PLANNING TECHNIQUES FOR AUTONOMOUS VEHICLES
Corresponding Author(s) : Pham Anh Phuong
paphuong@ued.udn.vn
UED Journal of Social Sciences, Humanities and Education,
Vol. 9 No. 1 (2019): UED JOURNAL OF SOCIAL SCIENCES, HUMANITIES AND EDUCATION
Abstract
While studying autonomous vehicles, we can see that each manufacturer and each project propose different control structures; however, they have the same basic operation structure for autonomous vehicles. Basing on this structure, developers make plans for their products. Due to technical, technological and legal difficulties and challenges, there have not been any effective solutions for autonomous vehicles so that they can operate on public roads. Therefore, with the aim of enhancing the ability to path planning based on the information received from traffic infrastructure system and other vehicles on the road through sensors and signal receiving systems, techniques for determining different path and motion control will be established based on the information obtained through sensors and signal receiving systems on autonomous vehicles, which enables autonomous vehicles to operate in mixed environments with strategies to improve its performance and optimize its operation process. In this paper, we evaluate the techniques for setting up the path planning studied recently. Then, we propose a solution and application research on autonomous vehicle problem.
Keywords
autonomous vehicle; path planning; motion planning; intelligent transportation systems.
Quach Hai Tho, & Pham Anh Phuong. (2019). OVERALL EVALUATION MOTION PLANNING TECHNIQUES FOR AUTONOMOUS VEHICLES. UED Journal of Social Sciences, Humanities and Education, 9(1), 23-32. https://doi.org/10.47393/jshe.v9i1.154
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References
-
[1] A. Nash, K. Daniel, S. Koenig et al (1999). Theta: Any-angle path planning on grids. Proceedings of the National Conference on Artificial Intelligence, 22, 2. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 2007, 1177.
[2] A. Stentz (1994). Optimal and efficient path planning for partially-known environments. Robotics and Automation, 1994. Proceedings., IEEE 3310-3317.
[3] A. Kelly, A. Stentz, O. Amidi et al (2006). Toward reliable off road autonomous vehicles operating in challenging environments. The International Journal of Robotics Research, 25, 5-6, 449-483.
[4] A. Piazzi, C. G. Lo Bianco, M. Bertozzi et al (2002). Quintic g2-splines for the iterative steering of vision-based autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 3, 27-36.
[5] D. Dolgov, S. Thrun, M. Montemerlo et al (2010). Path planning for autonomous vehicles in unknown semi-structured environments. The International Journal of Robotics Research, 29, no. 5, 485-501.
[6] D. Ferguson and A. Stentz (2006). Using interpolation to improve path planning: The field d* algorithm. Journal of Field Robotics, 23, 2, 79-101.
[7] D. Walton and D. Meek (2005). A controlled clothoid spline. Computers & Graphics, 29, 3, pp. 353-363.
[8] E. Krotkov, S. Fish, L. Jackel et al (2007). The darpa perceptor evaluation experiments. Autonomous Robots, 22, 1, 19-35.
[9] Y. Kuwata, S. Karaman, J.Teo et al (2009). Real-time motion planning with applications to autonomous urban driving. Control Systems Technology, IEEE Transactions on, 17, 5, 1105-1118.
[10] Y. K. Hwang and N. Ahuja (1992). Gross motion planninga survey. ACM Computing Surveys (CSUR), 24, 3, 219-291.
[11] J. Bohren, T. Foote, J. Keller et al (2008). Little ben: The ben franklin racing team’s entry in the 2007 darpa urban challenge. Journal of Field Robotics, 25, 9, 598-614.
[12] J. Funke, P. Theodosis, R. Hindiyeh et al (2012). Up to the limits: Autonomous audi tts. Intelligent Vehicles Symposium (IV), 2012 IEEE, 541-547.
[13] J. Reeds and L. Shepp (1990). Optimal paths for a car that goes both forwards and backwards. Pacific Journal of Mathematics, 145, 2, 367-393.
[14] J. Horst and A. Barbera (2006). Trajectory generation for an on-road autonomous vehicle. Defense and Security Symposium, international Society for Optics and Photonics, 62 302J-62 302J.
[15] J. M. Anderson, K. Nidhi, K. D. Stanley et al (2014). Autonomous Vehicle Technology: A Guide for Policymakers. Rand Corporation
[16] J. Perez, R. Lattarulo and F. Nashashibi (2014). Dynamic trajectory generation using continuous-curvature algorithms for door to door assistance vehicles. Intelligent Vehicles Symposium Proceedings, 2014 IEEE. IEEE, 510-515.
[17] J. van Dijke and M. van Schijndel (2012). Citymobil, advanced transport for the urban environment: Update. Transportation Research Record:Journal of the Transportation Research Board, 2324, 29-36.
[18] S. E. Shladover, C. A. Desoer, J. K. Hedrick et al (1991). Automated vehicle control developments in the path program. Vehicular Technology, IEEE Transactions on, 40, 1, 114-130.
[19] J. Y. Hwang, J. S. Kim, S. S. Lim, and K. H. Park (2003). A fast path planning by path graph optimization. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 33, 1, 121-129.
[20] J. Ziegler, M. Werling, and J. Schroder (2008). Navigating car-like robots in unstructured environments using an obstacle sensitive cost function. Intelligent Vehicles Symposium, 2008 IEEE. IEEE, 787-791.
[21] J. Ziegler, P. Bender, T. Dang et al (2014). Trajectory planning for bertha a local, continuous method. Intelligent Vehicles Symposium Proceedings, 2014 IEEE. IEEE, 450-457.
[22] J. Ziegler, P. Bender, M. Schreiber et al (2014). Making bertha drive an autonomous journey on a historic route. Intelligent Transportation Systems Magazine, IEEE, 6, 2, 8-20.
[23] J.w. Choi, R. Curry and G. Elkaim (2008). Path planning based on bezier curve for autonomous ground vehicles. World Congress on Engineering and Computer Science 2008, WCECS’08. Advances in Electrical and Electronics Engineering-IAENG Special Edition of the. IEEE, 158-166.
[24] K. Chu, M. Lee, and M. Sunwoo (2012). Local path planning for off-road autonomous driving with avoidance of static obstacles. Intelligent Transportation Systems, IEEE Transactions on, 13, 4, 1599-1616.
[25] K.Jo, M. Lee, D. Kim et al (2013). Overall reviews of autonomous vehicle a1- system architecture and algorithms. Intelligent Autonomous Vehicles, 8, 1, 114-119.
[26] K. Jo, J. Kim, D. Kim, C. Jang, and M. Sunwoo (2014). Development of autonomous car–part i: Distributed system architecture and development process. Industrial Electronics, IEEE Transactions on, 12.
[27] K.Kritayakirana and J. C. Gerdes (2012). Autonomous vehicle control at the limits of handling. International Journal of Vehicle Autonomous Systems, 10, 4, 271-296.
[28] K.Yang and S. Sukkarieh (2010). An analytical continuous-curvature path smoothing algorithm. Robotics, IEEE Transactions on, 26, 3, 561-568.
[29] L.Han, H.Yashiro, H. T. N. Nejad et al (2010). Bezier curve based path planning for autonomous vehicle in urban environ ment. Intelligent Vehicles Symposium (IV), 2010 IEEE. IEEE, 1036-1042.
[30] L.Labakhua, U. Nunes, R. Rodrigues et al (2008). Smooth trajectory planning for fully automated passengers vehicles: spline and clothoid based methods and its simulation. Informatics in Control Automation and Robotics. Springer, 169-182.
[31] L. Romani and M. Sabin (2004). The conversion matrix between uniform b-spline and bzier representations. Computer Aided Geometric Design, 21, 6, 549-560.
[32] M. Brezak and I. Petrovic (2014). Real-time approximation of clothoids with bounded error for path planning applications. Robotics, IEEE Transactions on, 30, 2, 507-515.
[33] M. Elbanhawi and M. Simic (2014). Sampling-based robot motion planning: A review. Access, IEEE, 2, 56-77.
[34] T. Berglund, A. Brodnik, H. Jonsson et al (2010). Planning smooth and obstacle-avoiding b-spline paths for autonomous mining vehicles. Automation Science and Engineering, IEEE Transactions on, 7, 1, 167-172.
[35] S. Han, B. Choi, and J. Lee (2008). A precise curved motion planning for a differential driving mobile robot. Mechatronics, 18, 9, 486- 494.
[36] S.M. LaValle (2006). Planning algorithms. Cambridge university press.
[37] S.M. LaValle and J. J. Kuffner (2001). Randomized kinodynamic planning. The International Journal of Robotics Research, 20, 5, 378-400.
[38] S.Glaser, B. Vanholme, S. Mammar et al (2010). Maneuver-based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction. Intelligent Transportation Systems, IEEE Transactions on, 11, 3, 589-606.
[39] V. Milanes, S. Shladover, J. Spring et al (2014). Cooperative adaptive cruise control in real traffic situations. Intelligent Transportation Systems, IEEE Transactions on, 15, 1, 296-305.
[40] V. Kunchev, L. Jain, V. Ivancevic, and A. Finn (2006). Path planning and obstacle avoidance for autonomous mobile robots: A review. Knowledge-Based Intelligent Information and Engineering Systems. Springer, 537-544.
[41] R. Kala and K. Warwick (2013). Multi-level planning for semi-autonomous vehicles in traffic scenarios based on separation maximization. Journal of Intelligent & Robotic Systems, 72, 3-4, 559-590.
[42] Z. Liang, G. Zheng, and J. Li (2012). Automatic parking path optimization based on bezier curve fitting. in Automation and Logistics (ICAL), IEEE International Conference on, Aug 2012, 583-587.