Full Text:   <1107>

Summary:  <1073>

CLC number: TP242.6

On-line Access: 2016-06-06

Received: 2015-08-18

Revision Accepted: 2016-01-29

Crosschecked: 2016-05-18

Cited: 0

Clicked: 3260

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yong-heng Jiang

http://orcid.org/0000-0002-9551-9846

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.6 P.553-565

http://doi.org/10.1631/FITEE.1500269


Intelligent computing budget allocation for on-road trajectory planning based on candidate curves


Author(s):  Xiao-xin Fu, Yong-heng Jiang, De-xian Huang, Jing-chun Wang, Kai-sheng Huang

Affiliation(s):  Department of Automation, Tsinghua University, Beijing 100084, China; more

Corresponding email(s):   fuxx10@mails.tsinghua.edu.cn, jiangyh@tsinghua.edu.cn

Key Words:  Intelligent computing budget allocation, Trajectory planning, On-road planning, Intelligent vehicles, Ordinal optimization


Xiao-xin Fu, Yong-heng Jiang, De-xian Huang, Jing-chun Wang, Kai-sheng Huang. Intelligent computing budget allocation for on-road trajectory planning based on candidate curves[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(6): 553-565.

@article{title="Intelligent computing budget allocation for on-road trajectory planning based on candidate curves",
author="Xiao-xin Fu, Yong-heng Jiang, De-xian Huang, Jing-chun Wang, Kai-sheng Huang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="6",
pages="553-565",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500269"
}

%0 Journal Article
%T Intelligent computing budget allocation for on-road trajectory planning based on candidate curves
%A Xiao-xin Fu
%A Yong-heng Jiang
%A De-xian Huang
%A Jing-chun Wang
%A Kai-sheng Huang
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 6
%P 553-565
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500269

TY - JOUR
T1 - Intelligent computing budget allocation for on-road trajectory planning based on candidate curves
A1 - Xiao-xin Fu
A1 - Yong-heng Jiang
A1 - De-xian Huang
A1 - Jing-chun Wang
A1 - Kai-sheng Huang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 6
SP - 553
EP - 565
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500269


Abstract: 
In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation (ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolution (OODE). The proposed algorithm is named IOODE with ‘I’ representing ICBA. OODE plans the trajectory in two parts: trajectory curve and acceleration profile. The best trajectory curve is picked from a set of candidate curves, where each curve is evaluated by solving a subproblem with the differential evolution (DE) algorithm. The more iterations DE performs, the more accurate the evaluation will become. Thus, we intelligently allocate the iterations to individual curves so as to reduce the total number of iterations performed. Meanwhile, the selected best curve is ensured to be one of the truly top curves with a high enough probability. Simulation results show that IOODE is 20% faster than OODE while maintaining the same performance in terms of solution quality. The computing budget allocation framework presented in this paper can also be used to enhance the efficiency of other candidate-curve-based planning methods.

This is a nice paper presenting useful and interesting algorithms and applications. The application is novel and highly interesting.

基于候选曲线的公路轨迹规划中的智能计算量分配

目的:公路轨迹规划是辅助驾驶和无人驾驶领域中的关键问题。为解决该问题,作者针对传统方法(如势场法、RRT)在求解质量和求解效率方面的不足,提出基于候选曲线的规划算法OODE。OODE分轨迹曲线和加速度变化两部分规划轨迹,采用差分进化(DE)算法通过求解子问题计算各候选曲线的评价,然后通过比较曲线评价从候选者中选取最优曲线。DE的迭代次数越多,曲线评价越准确。本文考虑对不同曲线智能分配迭代计算量,以减少总计算量消耗,同时保证所选最优曲线以足够高的概率是真实最优曲线,从而提高OODE算法的效率。
创新点:提出基于智能计算量分配(ICBA)的轨迹规划算法框架;设计曲线评价预测模型和优质曲线选拔模型,提出基于ICBA的轨迹规划算法IOODE。
方法:基于对优质曲线迭代分配计算量的思想,设计智能计算量分配(ICBA)机制,提出基于ICBA的轨迹规划算法框架(图4);设计曲线评价预测模型(EPM)和优质曲线选拔模型(CSM),提出基于ICBA的轨迹规划算法IOODE;通过仿真分析IOODE算法的轨迹规划结果(图9、10),验证所提出计算量分配机制的有效性(图12、13)和ICBA对算法效率的提升作用(图14、表5)。
结论:本文中提出的IOODE算法与OODE算法相比,求解质量没有明显区别,但求解速度提升约20%(表5)。

关键词:智能计算量分配;轨迹规划;公路规划;智能汽车;序优化

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Bai, L., Jiang, Y., Huang, D., 2012. A novel two-level optimization framework based on constrained ordinal optimization and evolutionary algorithms for scheduling of multipipeline crude oil blending. Ind. Eng. Chem. Res., 51(26):9078-9093.

[2]Bechhofer, R.E., Santner, T.J., Goldsman, D.M., 1995. Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons. Wiley, New York, USA.

[3]Bengler, K., Dietmayer, K., Farber, B., et al., 2014. Three decades of driver assistance systems: review and future perspectives. IEEE Intell. Transp. Syst. Mag., 6(4):6-22.

[4]Branke, J., Chick, S.E., Schmidt, C., 2007. Selecting a selection procedure. Manag. Sci., 53(12):1916-1932.

[5]Chen, C., Lee, L.H., 2010. Stochastic Simulation Optimization: an Optimal Computing Budget Allocation. World Scientific, USA.

[6]Chen, C., Yücesan, E., 2005. An alternative simulation budget allocation scheme for efficient simulation. Int. J. Simul. Process Model., 1(1/2):49-57.

[7]Chen, C., Lin, J., Yücesan, E., et al., 2000. Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discr. Event Dyn. Syst., 10(3):251-270.

[8]Chen, C., Chick, S.E., Lee, L.H., et al., 2015. Ranking and selection: efficient simulation budget allocation. In: Fu, M.C. (Ed.), Handbook of Simulation Optimization. Springer, New York, USA.

[9]Chick, S.E., Inoue, K., 2001. New two-stage and sequential procedures for selecting the best simulated system. Oper. Res., 49(5):732-743.

[10]Chu, K., Lee, M., Sunwoo, M., 2012. Local path planning for off-road autonomous driving with avoidance of static obstacles. IEEE Trans. Intell. Transp. Syst., 13(4):1599-1616.

[11]Fu, X., Jiang, Y., Huang, D., et al., 2015. A novel real-time trajectory planning algorithm for intelligent vehicles. Contr. Dec., 30(10):1751-1758 (in Chinese).

[12]Gehrig, S.K., Stein, F.J., 2007. Collision avoidance for vehicle-following systems. IEEE Trans. Intell. Transp. Syst., 8(2):233-244.

[13]Glaser, S., Vanholme, B., Mammar, S., et al., 2010. Maneuver-based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction. IEEE Trans. Intell. Transp. Syst., 11(3):589-606.

[14]Hilgert, J., Hirsch, K., Bertram, T., et al., 2003. Emergency path planning for autonomous vehicles using elastic band theory. Proc. IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, p.1390-1395.

[15]Ho, Y., Zhao, Q., Jia, Q., 2007. Ordinal Optimization: Soft Optimization for Hard Problems. Springer, New York, USA.

[16]Kim, S., Nelson, B.L., 2001. A fully sequential procedure for indifference-zone selection in simulation. ACM Trans. Model. Comput. Simul., 11(3):251-273.

[17]Köhler, S., Schreiner, B., Ronalter, S., et al., 2013. Autonomous evasive maneuvers triggered by infrastructure-based detection of pedestrian intentions. Proc. IEEE Intelligent Vehicles Symp., p.519-526.

[18]Kuwata, Y., Teo, J., Fiore, G., et al., 2009. Real-time motion planning with applications to autonomous urban driving. IEEE Trans. Contr. Syst. Technol., 17(5):1105-1118.

[19]Ma, L., Xue, J., Kawabata, K., et al., 2015. Efficient sampling-based motion planning for on-road autonomous driving. IEEE Trans. Intell. Transp. Syst., 16(4):1961-1976.

[20]McNaughton, M., Urmson, C., Dolan, J.M., et al., 2011. Motion planning for autonomous driving with a conformal spatiotemporal lattice. Proc. IEEE Int. Conf. on Robotics and Automation, p.4889-4895.

[21]Montemerlo, M., Becker, J., Bhat, S., et al., 2008. Junior: the Stanford entry in the urban challenge. J. Field Robot., 25(9):569-597.

[22]Papadimitriou, I., Tomizuka, M., 2003. Fast lane changing computations using polynomials. Proc. American Control Conf., p.48-53.

[23]Reif, J.H., 1979. Complexity of the mover’s problem and generalizations. Proc. 20th Annual Symp. on Foundations of Computer Science, p.421-427.

[24]Urmson, C., Anhalt, J., Bagnell, D., et al., 2008. Autonomous driving in urban environments: boss and the urban challenge. J. Field Robot., 25(8):425-466.

[25]Ziegler, J., Stiller, C., 2009. Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.1879-1884.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE