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基于轨迹分析的自主导航性能评估方法 被引量:2

Autonomous Navigation Performance Evaluation Method Based on Trajectory Analysis
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摘要 在马尔可夫决策模型框架下,提出一种基于轨迹分析的计算评估方法,通过分析驾驶回报设置和车辆轨迹的特征期望衡量自主导航系统的性能。假定回报函数是回报特征的线性组合,通过逼近不同的车辆自主驾驶策略,求解应用于沙盒场景的回报设置,从而仿真导航轨迹的特征期望。实验结果表明,该方法能对自主导航系统的轨迹数据实现定性和定量评估。 This paper proposes a novel trajectory-analysis-based performance evaluation method,which assesses and compares the navigation performances by reward settings and feature expectations under Markov Decision Process(MDP) framework.Assuming that the reward function is a linear combination of known features,this method evaluates reward settings by approximating the driving styles of different land autonomous navigation systems.On this basis,reward settings are used to simulate the feature expectations of navigation trajectories in a standard land navigation environment called the sandbox.Experimental results show the validity of this method in both qualitative and quantitative performance evaluations.
出处 《计算机工程》 CAS CSCD 北大核心 2011年第6期141-144,共4页 Computer Engineering
基金 国家自然科学基金资助项目(90820306)
关键词 轨迹分析 自主导航性能评估 马尔可夫决策过程 trajectory analysis autonomous navigation performance evaluation Markov Decision Process(MDP)
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参考文献28

  • 1O'day S. Metrics for Intelligent Autonomy[C]//Proc. of the Performance Metrics for Intelligent Systems Workshop. Gaithersburg, Maryland, USA: [s. n.], 2004.
  • 2Zadeh L A. The Search for Metrics of Intelligence -A Critical View[C]//Proc. of the Performance Metrics for Intelligent Systems Workshop. Gaithersburg, Maryland, USA: [s. n.], 2000.
  • 3Bien Z, Kim Y T. How to Measure the Machine Intelligence Quotient(MIQ): Two Methods and Applications[C]//Proc. of the World Automation Congress. Anchorage, Alaska, USA: [s. n.], 1998.
  • 4Huang Huimin. Autonomy Levels for Unmanned Systems(ALF US) Framework: Safety and Application Issues[C]//Proc. of the Performance Metrics for Intelligent Systems Workshop. Gaithersburg, Maryland, USA: [s. u.l, 2007.
  • 5Evans J M. Definitions and Measures of Intelligence in Deep Blue and the Army XUV[C]//Proc. of the Performance Metrics for Intelligent Systems Workshop. Gaithersburg, Maryland, USA: [s. n.], 2007.
  • 6Dufourd D, Dalgalarrondo A. Results and Lessons Learned from the Quantitative Evaluation of Road Detection and Tracking Algorithms[C]//Proc. of the Performance Metrics for Intelligent Systems Workshop. Gaithersburg, Maryland, USA: [s. n.], 2003.
  • 7Shirkhodaie A, Amrani R, Chawla N. Traversable Terrain Modeling and Performance Measurement of Mobile Robots[C]//Proc. of the Performance Metrics for Intelligent Systems Workshop.Gaithersburg, Maryland, USA: [s. n.], 2004.
  • 8Nau D, Ghallab M. Measuring the Performance of Automated Planning Systems[C]//Proc. of the Performance Metrics forIntelligent Systems Workshop. Gaithersburg, Maryland, USA: [s. n.], 2004.
  • 9Shneier M, Shackleford W, Tsai H. Performance Evaluation of a Terrain Traversability Learning Algorithm in the DARPA LAGR Program[C]//Proc. of the Performance Metrics for IntelligentSystems Workshop. Gaithersburg, Maryland, USA: [. n.], 2006.
  • 10Wagan A I, Godi A, Li Xiaolan. Map Quality Assessment[C]//Proc. of the Performance Metrics for Intelligent Systems Workshop. Gaithersburg, Maryland, USA: [s. n.], 2008.

同被引文献13

  • 1李德毅,刘常昱,杜鹢,韩旭.不确定性人工智能[J].软件学报,2004,15(11):1583-1594. 被引量:394
  • 2O'Day S, Steinberg M, Yglesias C, et al. Metrics for intelli- gent autonomy[C]//Performance Metrics for Intelligent Systems Workshop. Gaithersburg, USA: National Institute of Standards and Technology, 2004.
  • 3Huang H M, Albus J, Messina E, et al. Specifying autonomy levels for unmanned systems: Interim report[C]//Proceedings of SPIE, vol.5422. Bellingham, USA: SPIE, 2004: 386-397.
  • 4Huang H M, Pavek K, Ragon M, et al. Characterizing un- manned system autonomy: Contextual autonomous capabil- ity and level of autonomy analyses[C]//Proceedings of SPIE, vol.6561. Bellingham, USA: SPIE, 2007: 1-9.
  • 5Hadsell R, Sermanet P, Ben J, et al. Learning long-range vision for autonomous off-road driving[J]. Journal of Field Robotics, 2009, 26(2): 120-144.
  • 6Procopio M J, Mulligan J, Grudic G. Learning terrain segmen- tation with classifier ensembles for autonomous robot naviga- tion in unstructured environments[J]. Journal of Field Robotics, 2009, 26(2): 145-175.
  • 7Evans J M. Definitions and measures of intelligence in deep blue and the army XUV[C]//Performance Metrics for Intelligent Sys- tems Workshop. Gaithersburg, USA: National Institute of Stan- dards and Technology, 2007: 148-151.
  • 8王兆红,肖孟强,李燕,刘昕.类正态分布数据云模型的预测算法[J].计算机应用与软件,2009,26(9):78-79. 被引量:5
  • 9杨哲,张汝波.无人系统自主等级模糊评价方法[J].小型微型计算机系统,2009,30(10):2043-2047. 被引量:12
  • 10邸凯昌,李德毅,李德仁.云理论及其在空间数据发掘和知识发现中的应用[J].中国图象图形学报(A辑),1999,4(11):930-935. 被引量:119

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