基于吸收态马尔可夫链的智能无人车系统实时性能分析

Real-time Performance Analysis of Intelligent Unmanned Vehicle System Based on Absorbing Markov Chain

随着人工智能技术的进步和人机物融合系统的发展,智能无人车系统成为了新一代人工智能研究的前沿。智能无人车系统根据车辆和环境数据进行实时决策以控制无人车运行,因而该系统具有较高的实时性能要求,对系统实时性的分析是保障系统安全可靠的方法之一。为了对智能无人车系统的实时性能进行分析,以智能无人车变道系统为例,首先使用MARTE模型对智能无人车变道系统进行建模,在系统设计早期就引入性能需求参数;然后,通过模型转换将MARTE模型转化为吸收态马尔可夫链;最后,利用吸收态马尔可夫链的相关理论和公式综合估算了智能无人车系统的实时性能指标,并针对影响整个系统实时性的关键模块进行了分析。实验结果表明,文中所提模型和分析方法可以较好地分析智能无人车系统的实时性能。分析发现系统中智能模块的准确率与响应时间相互制约,在不同的运行场景下需要找到二者之间的平衡点以获得更优的实时性能。

With the advancements of artificial intelligence technology and the development of human-cyber-physical systems,intelligent unmanned vehicle systems are becoming the forefront of the new generation of artificial intelligence research.The intelligent unmanned vehicle system performs real-time decision based on vehicle and environmental data to control the unmanned vehicle.Therefore,the intelligent unmanned vehicle system has high real-time performance requirements.Analysis of the real-time performance of the system is one of the methods to ensure the safety and reliability of this kind of system.In order to analyze the real-time performance of the intelligent unmanned vehicle system,this paper takes the intelligent unmanned vehicle lane changing system as a scenario.First,the MARTE model is used to model the intelligent unmanned vehicle lane changing system,and the performance requirements parameters are added in the early system design.Then,through model transformation,the MARTE model is transformed into an absorption Markov chain.Finally,the relevant theories and formulas of the absorption Markov chain are used to comprehensively estimate the real-time performance indicators of the intelligent unmanned vehicle system,and analyze the key modules that affect the real-time performance of the entire system.The experimental results show that the model and analysis method proposed in the article can better analyze the real-time performance of the intelligent unmanned vehicle system.The analysis found that the accuracy and response time of the intelligent modules in the system restrict each other,and it is necessary to find a balance between the two in different operating scenarios to obtain better real-time performance.