Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches

It is known that adverse weather conditions can affect driver performance due to reduction in visibility and slippery surface conditions. Lane keeping is one of the main factors that might be affected by weather conditions. Most of the previous studies on lane keeping have investigated driver lane-keeping performance from driver inattention perspective. In addition, the majority of previous lane-keeping studies have been conducted in controlled environments such as driving simulators. Therefore, there is a lack of studies that investigate driver lane-keeping ability considering adverse weather conditions in naturalistic settings. In this study, the relationship between weather conditions and driver lane-keeping performance was investigated using the SHRP2 naturalistic driving data for 141 drivers between 19 and 89 years of age. Moreover, a threshold was introduced to differentiate lane keeping and lane changing in naturalistic driving data. Two lane-keeping models were developed using the logistic regression and multivariate adaptive regression splines (MARS) to better understand factors affecting driver lane-keeping ability considering adverse weather conditions. The results revealed that heavy rain can significantly increase the standard deviation of lane position (SDLP), which is a very widely used method for analyzing lane-keeping ability. It was also found that traffic conditions, driver age and experience, and posted speed limits have significant effects on driver lane-keeping ability. An interesting finding of this study is that drivers have a better lane-keeping ability in roadways with higher posted speed limits. The results from this study might provide better insights into understanding the complex effect of adverse weather conditions on driver behavior.     

Infrastructure assisted adaptive driving to stabilise heterogeneous vehicle strings

Literature has shown potentials of Connected/Cooperative Automated Vehicles (CAVs) in improving highway operations, especially on roadway capacity and flow stability. However, benefits were also shown to be negligible at low market penetration rates. This work develops a novel adaptive driving strategy for CAVs to stabilise heterogeneous vehicle strings by controlling one CAV under vehicle-to-infrastructure (V2I) communications. Assumed is a roadside system with V2I communications, which receives control parameters of the CAV in the string and estimates parameters imperfectly of non-connected automated vehicles. It determines the adaptive control parameters (e.g. desired time gap and feedback gains) of the CAV if a downstream disturbance is identified and sends them to the CAV. The CAV changes its behaviour based on the adaptive parameters commanded by the roadside system to suppress the disturbance.The proposed adaptive driving strategy is based on string stability analysis of heterogeneous vehicle strings. To this end, linearised vehicle dynamics model and control law are used in the controller parametrisation and Laplace transform of the speed and gap error dynamics in time domain to frequency domain enables the determination of sufficient string stability criteria of heterogeneous strings. The analytical string stability conditions give new insights into automated vehicular string stability properties in relation to the system properties of time delays and controller design parameters of feedback gains and desired time gap. It further allows the quantification of a stability margin, which is subsequently used to adapt the feedback control gains and desired time gap of the CAV to suppress the amplification of gap and speed errors through the string.Analytical results are verified via systematic simulation of both homogeneous and heterogeneous strings. Simulation demonstrates the predictive power of the analytical string stability conditions. The performance of the adaptive driving strategy under V2I cooperation is tested in simulation. Results show that even the estimation of control parameters of non-connected automated vehicles are imperfect and there is mismatch between the model used in analytical derivation and that in simulation, the proposed adaptive driving strategy suppresses disturbances in a wide range of situations.     

山区公路驾驶视觉信息量计算方法研究

为解决山区公路中驾驶视觉信息量难以量化的问题，对驾驶视野图像进行分割，根 据HSV颜色模型，提取视野图像的色调、饱和度、亮度值，再结合车速值，在驾驶视觉心理负荷的基础上，提出山区公路路域环境下的驾驶视觉信息量计算方法.通过实车实验，进行数据采集，并验证计算方法.计算结果表明，在半郁闭型空间行驶时，接收的视觉信息量最大；在郁闭型空间中，接收的信息量最小.计算结果与被试实际感受具有一致性，说明本文提出的驾驶视觉信息量计算方法具有可行性，可为路域环境的合理布设提供一定的技术参考.     

An empirical investigation on consumers’ intentions towards autonomous driving

Major steps towards implementation of autonomous and connected transport are being taken nowadays. The trend of automation technology being used in vehicles by the most important vehicle manufacturing industries is expected to move closer to high or fully Autonomous Vehicles (AVs) through technological advancements in sectors of robotics and artificial intelligence. Vehicles with autonomous driving capabilities are planning to be available on market, in full scale, in the next years. In the longer term substantial benefits are mainly expected for accessibility to transport, safety, traffic flow, emissions, fuel use and comfort. All these potential societal benefits will not be achieved unless AVs are accepted and used by a critical mass of people. Addressing these challenges, this paper: (a) proposes a technology acceptance modelling process by extending the original Technology Acceptance Model (TAM) to explain and predict consumers’ intensions towards AVs, (b) based on the proposed TAM-extended framework, a 30-question survey was conducted in order to investigate the factors influencing consumers’ intensions to use and accept AVs. Results show that the constructs of perceived usefulness, perceived ease to use, perceived trust and social influence, are all useful predictors of behavioral intentions to have or use AVs, with perceived usefulness having the strongest impact. The insights derived from this study could significantly contribute to ongoing research related to technology acceptance of AVs and are expected to allow automobile industries to improve their design and technology.     

基于自然驾驶数据的螺旋匝道(桥) 驾驶人心理负荷分析

为得到驾驶人在螺旋型立交匝道路段行车时的驾驶负荷及影响因素，选取4 座山地城市螺旋型立交开展自然驾驶实验，利用车载仪器采集自然驾驶习惯条件下的驾驶人心电信号. 分析心率幅值特征、心率连续差异均方根( RMSSD )整体分布特征，以及其与匝道半径、坡度之间的关系. 结果表明，影响心理负荷的主要因素是车辆行驶环境和匝道曲率变化. 匝道的分、合流鼻端处，以及行车过程中跟车、超车和会车均会使驾驶人心理负荷增大，驾驶人行驶在上坡路段的紧张感高于下坡路段，匝道半径与RMSSD 存在较强的负相关性. 纵坡坡度与心率变异指标之间呈现两种不同的相关关系：不熟练型驾驶人随着坡度增加，RMSSD 呈线性上升的趋势；一般型和熟练型驾驶人表现为中间高两边低的趋势.     

基于ANSYS的超声波激励下悬臂梁的动力学特性分析

文章针对原子力显微镜超声波激励模式下的振动行为运用ANSYS进行模态分析,并结合实验对悬臂梁上探针的振动频率对图像质量的影响进行研究分析,实验结果表明：超声激励的振动如果接近或等于悬臂梁的固有频率或低频状态下,图像的分辨率就会有明显的下降,而且振幅有很大偏差。因此,在实验过程中要避开悬臂梁的固有频率,就能提高成像质量。     

Influence of driving patterns on vehicle emissions: A case study for Latin American cities

On-board real-time emission experiments were conducted on 78 light-duty vehicles in Bogota. Direct emissions of carbon monoxide (CO), carbon dioxide (CO₂), nitrogen oxides (NO_x) and hydrocarbons (HC) were measured. The relationship between such emissions and vehicle specific power (VSP) was established. The experimental matrix included both gasoline-powered and retrofit dual fuel (gasoline–natural gas) vehicles. The results confirm that VSP is an appropriate metric to obtain correlations between driving patterns and air pollutant emissions. Ninety-five percent of the time vehicles in Bogota operate in a VSP between −15.2 and 17.7 kW ton⁻¹, and 50% of the time they operate between −2.9 and 1.2 kW ton⁻¹, representing low engine-load and near-idling conditions, respectively. When engines are subjected to higher loads, pollutant emissions increase significantly. This demonstrates the relevance of reviewing smog check programs and command-and-control measures in Latin America, which are widely based on static (i.e., idling) emissions testing. The effect of different driving patterns on the city’s emissions inventory was determined using VSP and numerical simulations. For example, improving vehicle flow and reducing sudden and frequent accelerations could curb annual emissions in Bogota by up to 12% for CO₂, 13% for CO and HC, and 24% for NO_x. This also represents possible fuel consumption savings of between 35 and 85 million gallons per year and total potential economic benefits of up to 1400 million dollars per year.     

基于注意力机制的多模态自动驾驶行为决策模型

针对现有端到端自动驾驶模型未考虑驾驶场景中不同区域的重要性和不同语义类别之间的关系而导致预测准确率低的问题，受驾驶人注意力机制和现有端到端自动驾驶模型的启发，充分考虑驾驶场景的动态变化、驾驶场景的语义信息和深度信息对驾驶行为决策的影响，以连续多帧驾驶场景的RGB图像为输入，构建一种基于注意力机制的多模态自动驾驶行为预测模型，实现对方向盘转角和车速的准确预测。首先，通过语义分割模型和单目深度估计模型分别获取RGB图像的语义图像和深度图像；其次，为剔除与驾驶行为决策无关信息，以神经科学和空间抑制理论为基础，设计一种拟人化注意力机制作为能量函数来计算驾驶场景中不同区域的重要度；为学习语义图像中与驾驶行为决策最为相关类别之间的关系，采用图注意力网络(Graph Attention Network,GAT)对驾驶场景的语义图像进行特征提取；然后，以保留RGB特征为原则对提取的驾驶场景的图像特征、语义特征和深度特征进行融合，采用卷积长短期记忆网络(Convolutional Long Short Term Memory,ConvLSTM)实现融合特征在连续多帧之间的传递，进而实现下一帧驾驶场景对应驾驶行为的预测；最后，与其他模型的对比试验、消融试验、泛化试验和特征可视化试验来充分验证所提出自动驾驶行为预测模型的性能。试验结果表明：与其他驾驶行为预测模型相比，所提出模型的训练误差为0.021 2，预测准确率为86.97%，均方误差为0.031 5，其驾驶行为的预测性能优于其他模型；连续多帧的语义图像和深度图像、拟人化注意力机制和面向语义特征提取的GAT有助于提升驾驶行为预测的性能；该模型具有较好的泛化能力,其做出驾驶行为预测所依赖的特征与经验丰富的驾驶人所关注的特征基本一致。     

Scheduling lane conversions for bus use on city-wide scales and in time-varying congested traffic

The paper explores what can occur when select street lanes throughout a city are periodically reserved for buses. Simulations of an idealized city were performed to that end. The city’s time-varying travel demand was studied parametrically. In all cases, queues formed throughout the city during a rush, and dissipated during the off-peak period that followed. Bus lanes were activated all at once across the city, and were eventually deactivated in like fashion. Activation and deactivation schedules varied parametrically as well. Schedules that roughly balanced the trip-time savings to bus riders against the added delays to car travelers were thus identified.Findings reveal why activating conversions near the start of a rush can degrade travel, both by car and by bus. Balance was struck by instead activating lane conversions nearer the end of the rush, when vehicle accumulation in the city was at or near its maximum. Most of the time savings to bus riders accrued after the conversions had been left in place for only 30 min. Leaving them for longer durations often brought modest additional savings to bus travelers. Yet, the added delays to cars often grew large as a result.These findings held even when buses garnered high ridership shares. This was the case when lane conversions gradually induced new bus trips among residents who formerly did not travel. It was also true when high ridership was a pre-existing feature of the city. Activating conversions a bit earlier in a rush was found to make sense only if commuters shifted from cars to buses in very large numbers. Findings also unveiled how to fine-tune activation and deactivation schedules to suit a city’s congestion level. Guidelines for scheduling conversions in real settings are furnished. So is discussion on how these schedules might be adapted to daily variations in city-wide traffic states. Roles for technology are discussed as well.     

Platoon based cooperative driving model with consideration of realistic inter-vehicle communication

Recent developments of information and communication technologies (ICT) have enabled vehicles to timely communicate with each other through wireless technologies, which will form future (intelligent) traffic systems (ITS) consisting of so-called connected vehicles. Cooperative driving with the connected vehicles is regarded as a promising driving pattern to significantly improve transportation efficiency and traffic safety. Nevertheless, unreliable vehicular communications also introduce packet loss and transmission delay when vehicular kinetic information or control commands are disseminated among vehicles, which brings more challenges in the system modeling and optimization. Currently, no data has been yet available for the calibration and validation of a model for ITS, and most research has been only conducted for a theoretical point of view. Along this line, this paper focuses on the (theoretical) development of a more general (microscopic) traffic model which enables the cooperative driving behavior via a so-called inter-vehicle communication (IVC). To this end, we design a consensus-based controller for the cooperative driving system (CDS) considering (intelligent) traffic flow that consists of many platoons moving together. More specifically, the IEEE 802.11p, the de facto vehicular networking standard required to support ITS applications, is selected as the IVC protocols of the CDS, in order to investigate how the vehicular communications affect the features of intelligent traffic flow. This study essentially explores the relationship between IVC and cooperative driving, which can be exploited as the reference for the CDS optimization and design.