大直径钢管桩吊打工艺沉桩技术运用

近年来,港口工程中大直径钢管桩的使用越来越普遍,但由于该类桩基施工需配备大型船舶设备,所需施工作业水域较大,很少在内河水域使用。介绍了在上海国际客运中心"小港池连接活动钢桥工程中",如何在狭小施工作业面进行大直径钢管桩沉桩的施工情况,分析了工程概况和难点,介绍了施工工艺的确定,施工准备和施工方法,有一定参考价值。     

钢板桩施工中锤型的选择及动力沉桩分析

结合八所港老港区1#~4#泊位改造工程,深入探讨了钢板桩的动力打入及振动沉桩机理,分别采用应力波动方程分析法和振型叠加法对该工程使用的U型单根钢板桩及组合式钢板桩进行了动力沉桩分析,并将计算分析结果与工程实测数据进行了对比。研究表明,动力打桩导致土体发生塑性流动或挤密侧移,而振动沉桩则使土体出现振动液化;应用有限差分法求解波动方程可以有效地模拟动力打桩过程,应用振型叠加法求解运动方程可以有效地模拟振动沉桩过程。     

Driving analytics using smartphones: Algorithms,comparisons and challenges

The present work investigates the use of smartphones as an alternative to gather data for driving behavior analysis. The proposed approach incorporates i. a device reorientation algorithm, which leverages gyroscope, accelerometer and GPS information, to correct the raw accelerometer data, and ii. a machine-learning framework based on rough set theory to identify rules and detect critical patterns solely based on the corrected accelerometer data. To evaluate the proposed framework, a series of driving experiments are conducted in both controlled and “free-driving” conditions. In all experiments, the smartphone can be freely positioned inside the subject vehicle. Findings indicate that the smartphone-based algorithms may accurately detect four distinct patterns (braking, acceleration, left cornering and right cornering) with an average accuracy comparable to other popular detection approaches based on data collected using a fixed position device.     

Traffic evacuation simulation based on multi-level driving decision model

Traffic evacuation is a critical task in disaster management. Planning its evacuation in advance requires taking many factors into consideration such as the destination shelter locations and numbers, the number of vehicles to clear, the traffic congestions as well as traffic road configurations. A traffic evacuation simulation tool can provide the emergency managers with the flexibility of exploring various scenarios for identifying more accurate model to plan their evacuation. This paper presents a traffic evacuation simulation system based on integrated multi-level driving-decision models which generate agents’ behavior in a unified framework. In this framework, each agent undergoes a Strategic, Cognitive, Tactical and Operational (SCTO) decision process, in order to make a driving decision. An agent’s actions are determined by a combination, on each process level, of various existing behavior models widely used in different driving simulation models. A wide spectrum of variability in each agent’s decision and driving behaviors, such as in pre-evacuation activities, in choice of route, and in the following or overtaking the car ahead, are represented in the SCTO decision process models to simulate various scenarios. We present the formal model for the agent and the multi-level decision models. A prototype simulation system that reflects the multi-level driving-decision process modeling is developed and implemented. Our SCTO framework is validated by comparing with MATSim tool, and the experimental results of evacuation simulation models are compared with the existing evacuation plan for densely populated Beijing, China in terms of various performance metrics. Our simulation system shows promising results to support emergency managers in designing and evaluating more realistic traffic evacuation plans with multi-level agent’s decision models that reflect different levels of individual variability of handling stress situations. The flexible combination of existing behavior and decision models can help generating the best evacuation plan to manage each crisis with unique characteristics, rather than resorting to a fixed evacuation plan.     

Connected cruise control among human-driven vehicles: Experiment-based parameter estimation and optimal control design

In this paper, we consider connected cruise control design in mixed traffic flow where most vehicles are human-driven. We first propose a sweeping least square method to estimate in real time feedback gains and driver reaction time of human-driven vehicles around the connected automated vehicle. Then we propose an optimal connected cruise controller based on the mean dynamics of human driving behavior. We test the performance of both the estimation algorithm and the connected cruise control algorithm using experimental data. We demonstrate that by combining the proposed estimation algorithm and the optimal controller, the connected automated vehicle has significantly improved performance compared to a human-driven vehicle.     

基于驾驶负荷的山区二级公路纵坡路段驾驶舒适度评价研究

为定量评价山区公路纵坡路段驾驶舒适度，本文以云南省文都二级公路为研究对象，进行实车试验开展驾驶负荷研究.首先，分析上、下坡路段坡度、坡长、加(减)速度指标对驾驶负荷影响程度，运用多元回归建立驾驶负荷模型；其次，利用心率增长率的第50、85分位值划分驾驶舒适度，结合所建模型对驾驶舒适度进行阈值分析，确定不同驾驶舒适程度对应坡度、加(减)速度区间范围；最后，对模型及阈值划分合理性进行分析验证.结果表明，坡度、加 (减)速度对纵坡路段驾驶负荷皆为显著变量，而坡长与驾驶负荷仅存在弱相关性.本文模型有效，阈值划分合理，可为低等级公路驾驶负荷研究提供参考.     

基于多模型融合的电动汽车行驶里程预测

纯电动汽车行驶里程预测是驾驶者最关心的问题之一，为解决现有预测算法模型精度低、相对误差大的问题，本文采用融合片段回归与单点分类的机器学习方法对行驶里程进行预测.以真实车辆各项状态参数、环境信息等作为输入，通过聚类和过滤封装式特征筛选，提取最优特征集合，并基于行驶片段样本量选择预测方法，通过对环境温度和电池健康状态(SOH)进行分层耦合提高片段回归预测精度，通过单点分类和片段回归预测模型融合优化最终预测结果.行驶里程测试集预测结果中均方根相对误差(RMSRE)为0.035，平均相对误差为1.71%，能够精确稳定地实现行驶里程预测.     

Personalised assistance for fuel-efficient driving

Recent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both drivers and passengers. In this article, we propose a reference architecture for a context-aware driving assistant system. Moreover, we exemplify this architecture with a real prototype of a driving assistance system called Driving coach. This prototype collects, fuses and analyses diverse information, like digital map, weather, traffic situation, as well as vehicle information to provide drivers in-depth information regarding their previous trip along with personalised hints to improve their fuel-efficient driving in the future. The Driving coach system monitors its own performance, as well as driver feedback to correct itself to serve the driver more appropriately.     

Interface design considerations for an in-vehicle eco-driving assistance system

This high-fidelity driving simulator study used a paired comparison design to investigate the effectiveness of 12 potential eco-driving interfaces. Previous work has demonstrated fuel economy improvements through the provision of in-vehicle eco-driving guidance using a visual or haptic interface. This study uses an eco-driving assistance system that advises the driver of the most fuel efficient accelerator pedal angle, in real time. Assistance was provided to drivers through a visual dashboard display, a multimodal visual dashboard and auditory tone combination, or a haptic accelerator pedal. The style of advice delivery was varied within each modality. The effectiveness of the eco-driving guidance was assessed via subjective feedback, and objectively through the pedal angle error between system-requested and participant-selected accelerator pedal angle. Comparisons amongst the six haptic systems suggest that drivers are guided best by a force feedback system, where a driver experiences a step change in force applied against their foot when they accelerate inefficiently. Subjective impressions also identified this system as more effective than a stiffness feedback system involving a more gradual change in pedal feedback. For interfaces with a visual component, drivers produced smaller pedal errors with an in-vehicle visual display containing second order information on the required rate of change of pedal angle, in addition to current fuel economy information. This was supported by subjective feedback. The presence of complementary audio alerts improved eco-driving performance and reduced visual distraction from the roadway. The results of this study can inform the further development of an in-vehicle assistance system that supports ‘green’ driving.