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Trajectory Planning for Automated Driving Based on ordinal optimization

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Tsinghua Science and Technology 2017年第1期22卷 62-72页

作者： Xiaoxin Fu Yongheng Jiang Dexian Huang Kaisheng Huang Jingchun Wang Department of Automation Tsinghua UniversityBeijing 100084 China Department of Automobile Engineering Tsinghua UniversityBeijing 100084 China

This paper proposes an approach based on ordinal optimization（OO） to solve trajectory planning for automated driving. As most planning approaches based on candidate curves optimize the trajectory curve and the velocity profile separately, this paper formulates the problem as an unified Non-Linear Programming（NLP） model,optimizing the trajectory curve and the acceleration profile（acceleration is the derivative of velocity） simultaneously.Then a hybrid optimization algorithm named OODE, developed by combining the idea of OO and Differential Evolution（DE）, is proposed to solve the NLP model. With the acceleration profile optimized ＂roughly＂, OODE computes and compares ＂rough＂（biased but computationally-easier） curve evaluations to select the best curve from candidates, so that a good enough curve can be obtained very efficiently. Then the acceleration profile is optimized again ＂accurately＂ with the selected curve. Simulation results show that good enough solutions are ensured with a high probability and our method is capable of working in real time.

关键词： ordinal optimization trajectory planning automated driving autonomous vehicle rough evaluation

Novel Models and Algorithms of Load Balancing for Variable-structured Collaborative Simulation under HLA/RTI

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Chinese Journal of Mechanical Engineering 2013年第4期26卷 629-640页

作者： YUE Yingchao FAN Wenhui XIAO Tianyuan MA Cheng State CIMS Engineering Research Center Tsinghua University

High level architecture(HLA) is the open standard in the collaborative simulation field. Scholars have been paying close attention to theoretical research on and engineering applications of collaborative simulation based on HLA/RTI, which extends HLA in various aspects like functionality and efficiency. However, related study on the load balancing problem of HLA collaborative simulation is insufficient. Without load balancing, collaborative simulation under HLA/RTI may encounter performance reduction or even fatal errors. In this paper, load balancing is further divided into static problems and dynamic problems. A multi-objective model is established and the randomness of model parameters is taken into consideration for static load balancing, which makes the model more credible. The Monte Carlo based optimization algorithm(MCOA) is excogitated to gain static load balance. For dynamic load balancing, a new type of dynamic load balancing problem is put forward with regards to the variable-structured collaborative simulation under HLA/RTI. In order to minimize the influence against the running collaborative simulation, the ordinal optimization based algorithm(OOA) is devised to shorten the optimization time. Furthermore, the two algorithms are adopted in simulation experiments of different scenarios, which demonstrate their effectiveness and efficiency. An engineering experiment about collaborative simulation under HLA/RTI of high speed electricity multiple units(EMU) is also conducted to indentify credibility of the proposed models and supportive utility of MCOA and OOA to practical engineering systems. The proposed research ensures compatibility of traditional HLA, enhances the ability for assigning simulation loads onto computing units both statically and dynamically, improves the performance of collaborative simulation system and makes full use of the hardware resources.

关键词： static load balancing dynamic load balancing variable-structure collaborative simulation under HLA/RTI multi-objective optimization ordinal optimization

Performance Improvement of Distributed Systems by Autotuning of the Configuration Parameters

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Tsinghua Science and Technology 2011年第4期16卷 440-448页

作者：张帆曹军威刘连臣吴澄 National CIMS Engineering and Research Center Tsinghua University Research Institute of Information Technology Tsinghua University Tsinghua National Laboratory for Information Science and Technology

The performance of distributed computing systems is partially dependent on configuration parameters recorded in configuration files. Evolutionary strategies, with their ability to have a global view of the structural information, have been shown to effectively improve performance. However, most of these methods consume too much measurement time. This paper introduces an ordinal optimization based strategy combined with a back propagation neural network for autotuning of the configuration parameters. The strat- egy was first proposed in the automation community for complex manufacturing system optimization and is customized here for improving distributed system performance. The method is compared with the covariance matrix algorithm. Tests using a real distributed system with three-tier servers show that the strategy reduces the testing time by 40% on average at a reasonable performance cost.

关键词： distributed systems performance evaluation autotune configuration parameters ordinal optimization covariance matrix algorithm

DESIGN SAMPLING AND REPLICATION ASSIGNMENT UNDER FIXED COMPUTING BUDGET

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Journal of Systems Science and Systems Engineering 2005年第3期14卷 289-307页

作者： Loo Hay LEE Ek Peng CHEW Department of Industrial & Systems Engineering National University of Singapore

For many real world problems, when the design space is huge and unstructured, and time consuming simulation is needed to estimate the performance measure, it is important to decide how many designs to sample and how long to run for each design alternative given that we have only a fixed amount of computing time. In this paper, we present a simulation study on how the distribution of the performance measures and distribution of the estimation errors/noises will affect the decision. From the analysis, it is observed that when the underlying distribution of the noise is bounded and if there is a high chance that we can get the smallest noise, then the decision will be to sample as many as possible, but if the noise is unbounded, then it will be important to reduce the noise level first by assigning more replications for each design. On the other hand, if the distribution of the performance measure indicates that we will have a high chance of getting good designs, the suggestion is also to reduce the noise level, otherwise, we need to sample more designs so as to increase the chances of getting good designs. For the special case when the distributions of both the performance measures and noise are normal, we are able to estimate the number of designs to sample, and the number of replications to run in order to obtain the best performance.

关键词： Ranking and selection ordinal optimization random sampling

Coordinated Design of Multiple Damping Controllers Based on Computational Intelligence

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Coordinated Design of Multiple Damping Controllers Based on ...