Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

Subjective-utility travel time budget modeling in the stochastic traffic network assignment

ABSTRACT

In this article, we propose a new model called subjective-utility travel time budget (SU-TTB) model to capture travelers' risk-averse route choices. In the travel time budget (TTB) and mean-excess travel time (METT) model, a predefined confidence level is needed to capture the risk-aversion in route choice. Due to the day-to-day route travel time variations, the exact confidence level is hard to be predicted. With the SU-TTB model, we assume travelers' confidence level belongs to an interval that they may comply with in the route choice. The two main components of SU-TTB are the utility function and the TTB model. We can show that the SU-TTB can be reduced to the TTB and METT model with proper utility function for the confidence levels. We can also prove its equivalence with our recently proposed nonlinear-expectation route travel time (NERTT) model in some cases and give some new interpretation on the NERTT with this equivalence. Finally, we formulate the SU-TTB model as a variational inequality (VI) problem to model the risk-averse user equilibrium (RAUE), termed as generalized RAUE (GRAUE). The GRAUE is solved via a heuristic gradient projection algorithm, and the model and solution algorithm are demonstrated with the Braess's traffic network and the Nguyen and Dupuis's traffic network.     

盾构下坡掘进对推进阻力的影响研究

以神华新街6°煤矿斜井盾构工程为背景,针对盾构下坡掘进工况,通过建立盾构上作用的水土压力载荷与坡度的数学关系模型,推导出推进阻力与坡度的数学公式。结合工程实例,计算水平掘进与6°下坡掘进各推进阻力值,并利用MATLAB软件绘制下坡掘进时推进阻力随坡度变化的曲线。结果表明:1)6°下坡掘进的总推进阻力相对于水平掘进仅减小了约8%,坡度小于13°时,可通过水平掘进时的推进阻力减去重力沿掘进轴线上的分量来近似求得,且偏差小于10%;2)总推进阻力会随坡角的不断增大而减小;3)当坡度大于50.4°后,盾构有自动向下滑移的趋势,刀盘将自动压紧开挖面,导致刀具自动嵌入开挖面,增加了启动扭矩与换刀的难度。     

框架可靠度计算的新方法

基于随机有限元的梯度优化算法引入拥有随机参数的框架可靠度分析.并编制计算程序与精确解Monte-Carlo法的计算结果进行了对比,证明该文方法用于框架结构可靠度分析是可行、可靠的,既避免了Monte-Carlo法计算量过大的缺点,又比传统的定值分析方法更合理,更贴近实际.     

金鸡滩二线船闸工程原型观测方法

水利枢纽船闸改扩建是内河航道整治工程中的控制性工程,以右江金鸡滩水利枢纽二线船闸工程原型观测实践为例,对船闸改扩建原型观测的方法进行分析和总结,主要包括比降观测、表面流速流向测量、断面流量和流速流向观测、泥沙测验和底质取样等5个观测方法。通过现场实践生产,在枢纽上下游多维度同步观测和采样,并对观测成果进行统计、分析和研究,为枢纽船闸改扩建的规划、设计和施工等建设技术方案提供水文基础资料,为模型试验、航道整治工程提供准确的工程实施数据,值得类似项目借鉴和应用。     

基于动态分频段FRA法的自耦变压器绕组轴向移位故障诊断

针对传统的频率响应分析(FRA)法无法识别自耦变压器绕组常见轴向移位故障的问题,提出基于动态分频段的FRA法:首先,搭建自耦变压器轴向移位故障模拟平台,获取不同轴向移位故障下绕组频响数据;其次,将幅频特性曲线波峰点与相频特性曲线C-L过零点频率相同的点作为预备分频点,同时将频响数据动态划分为多个频段,并绘制分频段极坐标图;然后,提取极坐标图的4个灰度梯度共生矩阵纹理特征和对应的归一化特征参数;最后,通过图形与特征的变化规律分析自耦变压器绕组的状态。试验验证结果表明:采用基于动态分频段的FRA法,生成的极坐标图数据点重叠情况得到改善,有利于图形分析和特征提取;不同绕组发生轴向移位故障时,各频段极坐标图变化趋势明显;同一绕组发生不同程度轴向故障时,极坐标图随故障程度增加差异呈变大的趋势;采用基于动态分频段的FRA法能准确区分自耦变压器轴向移位的故障绕组与故障程度,并能为自耦变压器现场故障诊断提供参考。     

大坡度引水隧道无轨运输出渣方式的应用

辽西北供水工程11#支洞，断面小、坡度大，出渣运输方式是决定工期的关键控制因素。采用无轨运输出渣方式，存在以下问题：隧道纵坡坡度较大，运输渣车爬坡困难、存在严重的安全隐患；断面小，自卸车在陡坡上运行和洞内调头困难等。对此采取扩大隧道开挖断面、设置避车洞，改装车辆、确保车辆制动的可靠性；加强安全管理措施等。实践证明，采用无轨运输方式出渣具有节约工期、经济效益显著等特点，可根据实际工况加以选用。     

温度应力对既有混凝土连续箱梁桥开裂的影响分析

采用三维空间实体单元，在分析连续箱梁桥温度应力分布规律的基础上，研究了温度梯度、箱梁的肋板与顶板刚度比以及跨径比等参数变化对温度应力的影响，并分析比较了按《公路桥涵设计通用规范》JTJ021-89版和JTG D60-2004版计算的温度应力。结果表明，在JTG D60-2004版温度梯度荷载作用下，箱梁顶板上下缘产生较大的横向拉应力，顶底板上下缘产生较大的纵向拉应力，产生较大温度应力处与实桥出现裂缝的部位基本吻合，从中揭示了温度应力对既有混凝土连续箱梁桥开裂的影响。     

混凝土箱梁桥断面竖向温度梯度的测试与分析

随着热量的流动,裂缝会出现和扩展,从而抑制热量流动,并引起结构的应力重分布。此外,对桥梁的温度分布及由此引起的变形规律的正确认识,有助于在施工中修正桥梁的定位偏差。该文介绍测试技术和热电偶在横向坡度4%的混凝土箱梁桥沿桥墩附近及四分之一跨截面的布置情况。当天气晴朗或多云时,修建期间桥面混凝土温度分布曲线不再是直线,而是呈抛物线,最大值出现在高度较高的腹板顶面。此外,该文通过对大量试验数据的分析,总结出桥梁断面温度梯度分布与气候条件的关系,这些气候资料来自现场实测和当地气象局。最后,根据气候条件提出在广州地区横坡为4%的混凝土箱梁桥的温度梯度模式。     

A model-based traction control strategy non-reliant on wheel slip information

A traction control system (TCS) for two-wheel-drive vehicles can conveniently be realised by means of slip control. Such a TCS is modified in this paper in order to be applicable to four-wheel-drive vehicles and anti-lock braking systems, where slip information is not readily available. A reference vehicle model is used to estimate the vehicle velocity. The reference model is excited by a saw-tooth signal in order to adapt the slip for maximum tyre traction performance. The model-based TCS is made robust to vehicle modelling errors by extending it with (i) a superimposed loop of tyre static curve gradient control or (ii) a robust switching controller based on a bi-directional saw-tooth excitation signal. The proposed traction control strategies are verified by experiments and computer simulations.