本文为基于MPC的基于MPC模型的增强学习方法的计划模块提出了一个新的评分功能,以解决使用奖励功能得分轨迹的固有偏见。所提出的方法使用折现价值和折扣价值提高了现有基于MPC的MBRL方法的学习效率。该方法利用最佳轨迹来指导策略学习,并根据现实世界更新其状态行动价值函数,并增强板载数据。在选定的Mujoco健身环境中评估了所提出方法的学习效率,以及在学习的模拟机器人模型中学习运动技能。结果表明,所提出的方法在学习效率和平均奖励回报方面优于当前的最新算法。
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由于交通的固有复杂性和不确定性,自主驾驶决策是一项具有挑战性的任务。例如,相邻的车辆可能随时改变其车道或超越,以通过慢速车辆或帮助交通流量。预期周围车辆的意图,估算其未来状态并将其整合到自动化车辆的决策过程中,可以提高复杂驾驶场景中自动驾驶的可靠性。本文提出了一种基于预测的深入强化学习(PDRL)决策模型,该模型在公路驾驶决策过程中考虑了周围车辆的操纵意图。该模型是使用真实流量数据训练的,并通过模拟平台在各种交通条件下进行了测试。结果表明,与深入的增强学习(DRL)模型相比,提出的PDRL模型通过减少碰撞数量来改善决策绩效,从而导致更安全的驾驶。
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为了计划安全的演习并采取远见卓识,自动驾驶汽车必须能够准确预测不确定的未来。在自主驾驶的背景下,深层神经网络已成功地应用于从数据中学习人类驾驶行为的预测模型。但是,这些预测遭受了级联错误的影响,导致长时间的不准确性。此外,学识渊博的模型是黑匣子,因此通常不清楚它们如何得出预测。相比之下,由人类专家告知的基于规则的模型在其预测中保持长期连贯性,并且是可解释的。但是,这样的模型通常缺乏捕获复杂的现实世界动态所需的足够表现力。在这项工作中,我们开始通过将智能驱动程序模型(一种流行的手工制作的驱动程序模型)嵌入深度神经网络来缩小这一差距。我们的模型的透明度可以提供可观的优势,例如在调试模型并更容易解释其预测时。我们在模拟合并方案中评估我们的方法,表明它产生了可端到端训练的强大模型,并无需为模型的预测准确性提供更大的透明度。
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Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.
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Fuzzy logic has been proposed in previous studies for machine diagnosis, to overcome different drawbacks of the traditional diagnostic approaches used. Among these approaches Failure Mode and Effect Critical Analysis method(FMECA) attempts to identify potential modes and treat failures before they occur based on subjective expert judgments. Although several versions of fuzzy logic are used to improve FMECA or to replace it, since it is an extremely cost-intensive approach in terms of failure modes because it evaluates each one of them separately, these propositions have not explicitly focused on the combinatorial complexity nor justified the choice of membership functions in Fuzzy logic modeling. Within this context, we develop an optimization-based approach referred to Integrated Truth Table and Fuzzy Logic Model (ITTFLM) that smartly generates fuzzy logic rules using Truth Tables. The ITTFLM was tested on fan data collected in real-time from a plant machine. In the experiment, three types of membership functions (Triangular, Trapezoidal, and Gaussian) were used. The ITTFLM can generate outputs in 5ms, the results demonstrate that this model based on the Trapezoidal membership functions identifies the failure states with high accuracy, and its capability of dealing with large numbers of rules and thus meets the real-time constraints that usually impact user experience.
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为连接和自动化车辆(CAVS)开发安全性和效率应用需要大量的测试和评估。在关键和危险情况下对这些系统运行的需求使他们的评估负担非常昂贵,可能危险且耗时。作为替代方案,研究人员试图使用仿真平台研究和评估其算法和设计。建模驾驶员或人类操作员在骑士或其他与他们相互作用的车辆中的行为是此类模拟的主要挑战之一。虽然为人类行为开发完美的模型是一项具有挑战性的任务和一个开放的问题,但我们展示了用于驾驶员行为的模拟器中当前模型的显着增强。在本文中,我们为混合运输系统提供了一个模拟平台,其中包括人类驱动和自动化车辆。此外,我们分解了人类驾驶任务,并提供了模拟大规模交通情况的模块化方法,从而可以彻底研究自动化和主动的安全系统。通过互连模块的这种表示形式提供了一个可以调节的人解剖系统,以代表不同类别的驱动程序。此外,我们分析了一个大型驾驶数据集以提取表达参数,以最好地描述不同的驾驶特性。最后,我们在模拟器中重新创建了类似密集的交通情况,并对各种人类特异性和系统特异性因素进行了彻底的分析,研究了它们对交通网络性能和安全性的影响。
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本文提出了一个理论和计算框架,用于基于非欧几里得收缩理论对隐式神经网络的训练和鲁棒性验证。基本思想是将神经网络的鲁棒性分析作为可及性问题,使用(i)$ \ ell _ {\ infty} $ - norm inort input-utput-optup-utput lipschitz常数和(ii)网络的紧密包含函数到过度陈列在其可达集合中。首先,对于给定的隐式神经网络,我们使用$ \ ell _ {\ infty} $ - 矩阵测量方法来为其适应性良好的条件提出足够的条件,设计一种迭代算法来计算其固定点,并为其$ \提供上限ell_ \ infty $ -Norm输入输出Lipschitz常数。其次,我们介绍了一个相关的嵌入式网络,并表明嵌入式网络可用于提供原始网络的可触及式集合的$ \ ell_ \ infty $ -Norm Box过度交配。此外,我们使用嵌入式网络来设计一种迭代算法,用于计算原始系统紧密包含函数的上限。第三,我们使用Lipschitz常数的上限和紧密包含函数的上限来设计两种算法,以训练和稳健性验证隐式神经网络。最后,我们应用算法在MNIST数据集上训练隐式神经网络,并将模型的鲁棒性与通过文献中现有方法训练的模型进行比较。
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人类使用未知的相似性函数在未标记的数据集中天生测量实例之间的距离。距离指标只能作为相似实例信息检索相似性的代理。从人类注释中学习良好的相似性功能可以提高检索的质量。这项工作使用深度度量学习来从很少的大型足球轨迹数据集中学习这些用户定义的相似性功能。我们将基于熵的活跃学习方法从三重矿山开采进行了最新的工作,以收集易于招募的人,但仍来自人类参与者提供信息的注释,并利用它们来训练深度卷积网络,以概括为看不见的样本。我们的用户研究表明,与以前依赖暹罗网络的深度度量学习方法相比,我们的方法提高了信息检索的质量。具体而言,我们通过分析参与者的响应效率来阐明被动抽样启发式方法和主动学习者的优势和缺点。为此,我们收集准确性,算法时间的复杂性,参与者的疲劳和时间响应,定性自我评估和陈述以及混合膨胀注释者的影响及其对模型性能和转移学习的一致性。
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深度学习(DL)算法在不同领域显示出令人印象深刻的性能。其中,由于一些有趣的模式,在过去的几十年中,音频吸引了许多研究人员 - 尤其是在音频数据的分类中。为了更好地执行音频分类,功能选择和组合起着关键作用,因为它们有可能制造或破坏任何DL模型的性能。为了调查这一角色,我们对具有各种最先进的音频特征的多种尖端DL模型(即卷积神经网络,Extricnet,Mobilenet,Supper Vector Machine和Multi-Pecceptron)的性能进行了广泛的评估。 (即MEL频谱图,MEL频率Cepstral系数和零交叉率)在三个不同的数据集上独立或作为组合(即通过结合)(即免费的口语数据集,音频urdu数据集和Audio Gujarati Digits Digaset数据集) )。总体而言,结果建议特征选择取决于数据集和模型。但是,特征组合应仅限于单独使用时已经实现良好性能的唯一特征(即主要是MEL频谱图,MEL频率Cepstral系数)。这种功能组合/结合使我们能够胜过以前的最新结果,而与我们选择的DL模型无关。
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We study experiment design for unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skeleton of causal graphs with cycles may not be possible from merely the observational distribution. Furthermore, intervening on a variable in such graphs does not necessarily lead to orienting all the edges incident to it. In this paper, we propose an experiment design approach that can learn both cyclic and acyclic graphs and hence, unifies the task of experiment design for both types of graphs. We provide a lower bound on the number of experiments required to guarantee the unique identification of the causal graph in the worst case, showing that the proposed approach is order-optimal in terms of the number of experiments up to an additive logarithmic term. Moreover, we extend our result to the setting where the size of each experiment is bounded by a constant. For this case, we show that our approach is optimal in terms of the size of the largest experiment required for uniquely identifying the causal graph in the worst case.
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