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雷达卡
摘要翻译：
本文致力于开发自动驾驶车辆接近交通灯时的最佳加速度/速度剖面。设计目标是既能缩短行车时间，又能降低能耗，还能避免红灯时空转。这是通过充分利用基于车辆到基础设施通信的交通灯信息来实现的。将该问题建模为混合整数规划，通过放松整数约束将其等价转化为最优控制问题。然后，利用直接邻接法求解状态约束下的自由和固定终端时间最优控制问题。通过详细的分析，我们能够产生一个实时在线的解析解，将我们的方法与现有的基于数值计算的方法区别开来。通过大量的仿真，比较了自动驾驶车辆和人工驾驶车辆在所提出的速度分布下的性能。结果表明，该算法在能量消耗和旅行时间方面具有一定的优势。
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英文标题：
《Optimal Control of Autonomous Vehicles Approaching A Traffic Light》
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作者：
Xiangyu Meng, and Christos G. Cassandras
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
This paper devotes to the development of an optimal acceleration/speed profile for autonomous vehicles approaching a traffic light. The design objective is to achieve both short travel time and low energy consumption as well as avoid idling at a red light. This is achieved by taking full advantage of the traffic light information based on vehicle-to-infrastructure communication. The problem is modeled as a mixed integer programming, which is equivalently transformed into optimal control problems by relaxing the integer constraint. Then the direct adjoining approach is used to solve both free and fixed terminal time optimal control problems subject to state constraints. By an elaborate analysis, we are able to produce a real-time online analytical solution, distinguishing our method from most existing approaches based on numerical calculations. Extensive simulations are executed to compare the performance of autonomous vehicles under the proposed speed profile and human driving vehicles. The results show quantitatively the advantages of the proposed algorithm in terms of energy consumption and travel time.
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PDF链接：
https://arxiv.org/pdf/1802.096
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