In order to improve the driving safety of the freeway tunnel section, the research on zoning section speed limit is carried out. Based on the visual characteristics of tunnel drivers and its influence on traffic safety, this paper establishes the design system of speed limit section in tunnel, divides the speed limit section in tunnel, analyzes the acceptability of drivers to the speed limit setting by questionnaire method, and gives an example application. The design method can provide a basis for the design of speed limit in tunnel section.

Human intervention is critical in situations where the autonomous system issues a takeover request in conditionally driving. Effective human-machine collaboration and safe transition require the timely and accurate identification of the driver's takeover intentions. Given the challenges associated with the opacity of decision-making processes in standalone end-to-end models for inferring takeover intentions, this paper introduces an approach for inferring takeover intention based on driver-hand trajectory estimation, which leverages the physical and observable actions as an interpretable indicator. We first presented a comprehensive framework designed to acquire preliminary data on the driver's hand and steering wheel position. This framework utilized MediaPipe for the acquisition of hand coordinates, EfficientDet for steering wheel localization, and MiDaS for depth estimation. Then, the Extended Kalman Filter (EKF) model was integrated into the Gated Recurrent Unit (GRU) network (GRU-EKF) to estimate hand movements and subsequently predict takeover intention combining current-time trajectory with estimated future states. The approach was validated through simulated experiments of human-machine driving interaction. In addition, a further examination is conducted to analyze the influence of six distinct combinations of historical and estimation horizons on model performance. Results show that the GRU-EKF methodology outperforms other models in terms of overall performance metrics. Using shorter estimation horizons can lead to higher accuracy and precision, whereas increasing the estimation horizon to 3 s notably improves the model's ability to correctly classify different takeover intentions. The findings can offer valuable perspectives for the design and enhancement of takeover process functionalities of Advanced Driver-Assistance Systems.

As an important intersection of expressway and other roads, the safe and efficient operation of the confluence area is directly related to the capacity and efficiency of the entire expressway network. Based on driver's visual perception characteristics of induction facilities, this paper gives the setting method for intelligent induction facilities within the confluence area, determines the optimal layout interval of mainline and ramp intelligent induction facilities, optimizes the induction information release in the confluence area, and determines the speed induction strategy under different traffic volumes of the mainline and ramp in the confluence area. Using SCANeR Studio software to verify the effectiveness of the intelligent traffic induction scheme. Results show that the scheme can effectively induce the vehicle and improve the traffic condition of the confluence area. This study has important theoretical and practical significance for improving the traffic efficiency and safety level of the confluence area.

Lane-changing behaviour is more complex and riskier than lane-keeping, posing challenges for autonomous vehicle decision-making. To address this issue, this study modelled the decision-making process of lane-changing behaviour by combining Stackelberg game theory with the potential field method. The potential field method analyzes dynamic interactions between multiple vehicles, while game theory quantifies the efficiency and safety benefits of lane-changing. Subsequently, a decision-making model for multi-vehicle interaction was established by integrating game theory and the potential field method. Finally, the lane-changing trajectory was optimised for maximum driving benefit. A simulation of lane-changing behaviour was used to validate the performance of the proposed model. The results indicated that the proposed method leads to a higher average velocity (20.07 m/s), faster lane changing (3.14 s) and lower computational costs (0.075 s) than the rule-based method (19.21 m/s, 4.04 and 0.17 s, respectively). This finding can provide a reference for reasonable decision-making for autonomous vehicles.

In order to find out the road sections that may have traffic safety hazards in the design stage of expressway, this study carried out the design of expressway safety evaluation system for the design stage. Based on the analysis of system requirements, requirements objectives and workflow, the safety evaluation system of highway design stage is designed, including database module, specification compliance review module, design consistency evaluation module, accident prediction module, map operation module and user operation module. In this study, a safety evaluation model based on design consistency and accident prediction factors is presented, utilizing the integration of MapInfo and SQL Server database. The model library is constructed, and subsequently, standard compliance review and design consistency evaluation are conducted. The system can realize visualization and docking with road design software. The adjustment of design scheme and data statistics and analysis can be realized by using the callback technology of MapInfo. The design, evaluation and optimization of expressway can be coordinated synchronously, and the safety of expressway in design and operation stage can be evaluated.

Conditional Automated Driving (CAD) has attracted widespread attention due to the substantial gap in achieving fully autonomous driving, wherein an essential endeavor entails determining the transition timing between automated and manual driving modes. Driver cognitive workload serves as a crucial indicator for identifying transition timing, while its precise determination is challenging with discrete workload levels in previous studies. To address this issue, this work develops a dual-stage learning framework to quantify driver cognitive workload continuously. Specifically, a semi-supervised co-training strategy is first designed to approximate workload values, and then supervised contrastive learning is employed to align them with their feature representations in the latent space. A novel driver workload dataset is constructed for the evaluation, and experimental results demonstrate that our proposed approach outperforms other state-of-the-art baselines in estimation accuracy. Furthermore, the rationality of quantified cognitive workload is analyzed through the driver’ subjective assessment, indicating it is a more reliable solution for achieving the driving authority transition.

To assess the operational risk of multi-vehicle interactions for connected and autonomous vehicles (CAVs) in the weaving area, this study defines the ‘multi-vehicle interaction risk potential field’ based on the similarity between vehicle risk and potential field, on the basis of which the risk potential field between the interactive CAVs and ego CAV is determined. Then, a real-time risk assessment model of CAVs in the weaving area is developed, and a surrogate safety measure R of the CAVs’ operational risk is proposed. Subsequently, the kinematic data of CAVs are obtained by simulation. Furthermore, the genetic algorithm is used to calibrate the parameters of the real-time risk assessment model. The validity of the model is demonstrated by comparing the R value and traffic conflict indicator (TTCi) for the car-following and cut-in behaviour in the weaving area scenario. The validation results of typical cases show that the variation ranges of R in the car-following and cut-in scenarios are 2.2 and 1.9 times those of TTCi, respectively, which can more effectively denote the operational risk of CAVs during multi-vehicle interactions in weaving area. The study can be used to provide reference for CAVs’ driving decisions in weaving areas and promote their safety in multi-vehicle interaction scenarios.

Drivers in highly automated vehicles (AVs) are not required to continuously monitor the AV system. However, they must be prepared to take over vehicles when requested. Therefore, it is necessary to design an in-cabin interface that allows drivers to adapt their levels of preparedness to the likelihood of control transition. This study examined the interaction effects of (1) takeover request modality (TORM), (2) non-driving related tasks (NDRT), (3) takeover lead time (TOLT) on takeover performance. We conducted a driver-in-the-loop experiment involving 32 participants based on 15 takeover requests (TORs) for each NDRT. The Particle swarm optimization algorithm combined with multilayer perceptron learning was used for multi-objective balance optimization of five performance indicators. Results showed that the utilization of tactile-auditory prompts with 5 s and 9 s TOLTs exhibited positive performance in music listening and no task situations. The integration of tactile-auditory or tactile-visual cues with 7 s and 9 s TOLTs yielded favorable results in reading scenarios, whereas the tactile cues demonstrated efficacy in video watching scenarios. The situation of visual with 5 s TOLT reached the optimal balance of the five optimization objectives when the driver performed no task. This finding can offer valuable guidance to design interfaces in highly automated vehicles.

In conditionally automated driving, traffic safety problems would occur if the driver does not properly take over the control authority when the request of automated system arises. Therefore, this study proposes XGBoost learning method considering risk potential field to predict the takeover quality in conditionally automated driving under different levels of cognitive non-driving related tasks (NDRTs). Thirty participants drive on two experimental conditions: manual driving is following an automated driving during which the driver is asked to perform NDRTs. Drivers’ physiological features of different cognitive states are exploited to model multi-level takeover quality prediction. This investigation also gives an insight into the main effects of the selected prediction variables on the takeover quality. The proposed model performance within different time windows is assessed using multiple evaluation metrics and compared with other methods. Results show that the prediction accuracy within the time windows of 7–10 s, 5–7 s, 3–5 s and 1–3 s is 0.87, 0.85, 0.85 and 0.90, respectively. The XGBoost model has the best performance of different time windows under each level of takeover quality compared to the other three machine learning models. Our findings can effectively predict the takeover quality and assist automated driving safely in interactions between drivers and automated vehicles.

Drivers will be required to maintain readiness to take over manual control of the automated vehicle occasionally during the highly automated driving until the advent of fully vehicle automation. Users can engage in cognitive tasks in this level of automated driving. However, it is unclear whether the engagement in cognitive secondary activities would impair or increase the driver's capacity to take over control authority. To explore such issue, four highly automated takeover scenarios were designed to study the individual and interaction effects of secondary tasks and time windows on takeover performance. Thirty participants engaged in similar cognitive tasks under different levels. A control group did not conduct any secondary tasks. Drivers had to resume vehicle control in four different scenarios while engaging in secondary tasks. The transition situation happened when a broken-down vehicle suddenly appears in front of the ego vehicle. Results show that there is significant difference in brake force, lateral offset and pitch, and no significant difference in sideslip angle-0 left, gas pedal, yaw speed and roll speed under different cognitive tasks. The takeover time under the condition of 0-back cognitive task is higher than that of no task (control group) and other two tasks, indicating that the proper workload of secondary task enhance alertness compared with the no-task, and high workload of secondary task could increase reaction time of takeover. The interaction effect between secondary task and time window on acceleration and brake force is not obvious. The significant difference exists in both individual and interaction effect of secondary task and time window on lateral offset and pitch. The findings could potentially provide reference for the design of safe and efficient highly automated driving systems during transition process.

针对生产过程中单一物料配送方式所导致的成本过高问题,开展考虑成本最优的物料配送方式研究,基于批量配送和kit配送方式,以物流配送的总成本最优为目标,构建改进的物料配送方式组合优化模型.以汽车生产车间装配线为例,给出物料配送最优方案并进行有效性验证,结果表明:与批量配送方式或kit配送的方式相比,改进的物料配送方式组合优化模型可以分别节约83.6％和70.8％的物流成本,且具有较好的鲁棒性;相比于纸箱装载的物料,以托盘形式装载的物料有更大概率会选择kit配送方式;在全约束条件限制的情况下,物料更倾向于采用kit配送的方式.影响配送方式组合优化的因素优先级为线边堆放区总面积、牵引车额定载重、叉车额定载重,研究结果可为生产车间管理者提供建议.

The risky lane-changing manoeuvre of vehicles often occurs at expressway entrances, which would result in a high crash risk in the freeway system and significantly impact its safety. The highly anticipated environment of connected and autonomous vehicles (CAVs) is expected to reduce the associated crash risk of lane changing by offering various types of driving support, which utilise surrounding traffic information. The modelling crash risk under the environment of CAV driving during mandatory lane changing in merging areas faces new challenges due to the novelty of CAVs and subsequent shortage of data. To explore such risk situation of multi-vehicle interaction at expressway entrances, this study proposed a supervised learning algorithm and a Bayesian hierarchical model to assess risk levels and predict the probability of risk occurrence at different risk levels of interactive vehicles in real time of mandatory driving behaviour during the merging process. The learning algorithm, based on XGBoost, was exploited to classify risk levels. The Bayesian hierarchical model was used to analyse the probability of real-time risk comprising vehicle physical state layer, multi-vehicle interaction layer and risk probability layer. The probabilistic model parameters were calibrated using Markov Chain Monte Carlo (MCMC) Gibbs sampling method. The K-fold cross validation method was used to validate the proposed model of risk level. The probabilistic model validity was tested through posterior prediction of P-value. The quantitative risk estimation of CAVs through a few merging cases was conducted. Results show that the identification accuracy of slight, low, moderate and high risk is 94.24%, 85.82%, 84.16% and 79.69%, respectively. The P-value of Durbin-Watson's posterior, normal hypothesis, test distribution symmetry and kurtosis are all close to 0.5. Therefore, the method of real-time risk assessment is convergent and has good fitting. This research can promote cautious driving behaviours and provide reference for driver's decision making in the long term under the environment of CAVs.

In order to identify the segments of China's highways that may have traffic safety hazards in the design phase, reduce the traffic accident rate and improve the operational safety of vehicles. This paper constructs a safety evaluation model based on design consistency applicable to China's highways, including the operating speed prediction model and design consistency evaluation indexes. Combined with actual cases, the effectiveness of the model is verified by comparing the constructed model, IHSDM and the evaluation method of “Specifications for Highway Safety Audit”. The results show that the accuracy of the model is 42% higher compared with IHSDM, and it can effectively evaluate the design safety of China's highways. It has important theoretical and practical significance for the study of highway design safety evaluation in China.

To quantitatively analyze velocity change in the interaction of multiple vehicles during the lane-changing process, this study defines the concepts of driving constraint region and multi-vehicle interaction region (MVIR) by analyzing the potential energy field. The attraction and repulsion effects of the target vehicle and surrounding vehicles are attributed to the area change in the overlapping parts of their MVIRs. A multi-vehicle interactive lane-changing method considering MVIR is proposed. A velocity change model of multi-vehicle interactive lane-changing is established to reveal the change regularity of velocity under multi-vehicle interactive lane-changing. The model parameters are calibrated using a P3-DT Beidou high-precision positioning direction finding receiver to collect vehicle coordinates and velocity. The error of vehicle velocity variation was less than 11%, which verified the validity of the velocity change model. The research results can guide drivers in completing the lane-changing process safely and quickly, and provide theoretical support for micro-traffic flow simulation, road traffic safety proactive prevention and control, multi-vehicle lane-changing rules, and multi-agent simulation platform construction.

To study the influence of the coupling relationship between primary and secondary tasks on driving safety, four driving experiments are designed, including Bluetooth calling, conversation, screen touch operation and in-vehicle radio operation. The vehicle operation data such as standard deviation of horizontal speed, standard deviation of longitudinal speed and steering entropy are collected. The driver visual data such as entropy rate of fixation area, standard deviation of horizontal viewing angle and vertical viewing angle, and average glance speed are collected. The coupling model of primary and secondary tasks in the vehicle is established to determine the coupling coordination degree between them. The driving proportion threshold of driver's secondary tasks and the risk level of various secondary tasks are obtained. The results show that the coupling degree of normal driving and Bluetooth communication can reach high quality coordination level with high safety. Similarly, the coordination level between normal driving and talking is also high. The coupling degree of normal driving and operating radio can reach medium coordination level with general safety. The coupling degree of normal driving and touch screen operation can only achieve low coordination, which is a relatively dangerous driving state.

In order to solve the problem of coordination between speed and alignment design of expressway in China, and improve the safety of expressway operation. On the basis of determining the operation safety evaluation index, combined with the existing domestic traffic safety evaluation research data, referring to the American interactive highway safety design model (IHSDM), this paper build an improved highway alignment operation safety evaluation model considering design consistency. A case study is carried out to verify the effectiveness of the model. The results show that the accuracy of the model is 83.33%, which can effectively evaluate the operation safety of highway alignment design, and the research results are positive. It is of great theoretical and practical significance to guide the safety evaluation of highway alignment operation in China.

To better construct the coordinated development of the comprehensive transportation system, the prediction model of comprehensive freight index in China was studied. Basic indicators of production and supply, benefits and macroeconomic were selected. Cluster analysis was used to divide the indicators into 13 leading indicators and 13 synchronous indicators. Factor analysis was used to analyze the importance of each leading indicator. The characteristic quantities of freight index were determined and the Granger causality test was conducted between the four indexes and the five characteristic quantities respectively. The relationship model between each freight index and characteristic quantity was constructed, and the validity of the model was verified. Results show that the freight indexes are volatile. There is a stable relationship between characteristic quantity and freight index. The error of forecast and actual value of is within 10%, verifying the validity of the model. The results can provide theoretical support for forecasting the development trends of freight transport among various transport modes. The comprehensive transport will be improved to provide a decision-making basis for the national macro-department to make policies.

为减少低能见度下无信号交叉口过街行人与车辆的交通事故,开展了考虑能见度影响的车辆与过街行人冲突识别研究.结合车辆行人相对位置、速度、加速度、车辆尺寸等信息,构建了过街行人与车辆冲突识别模型,确定了基于人-车间距的交通环境能见度测量方法,给出车辆速度与能见度之间的关系模型,在此基础上对模型进行修正,并验证了模型的有效性.结果表明:该冲突识别模型可对过街行人与车辆冲突进行有效识别,冲突识别的准确率为82.4％,该研究可为车-路协同下的无信号交叉口行人和车辆冲突识别提供决策,进而提高低能见度下行人与车辆的安全性.

为满足高速公路运行车速与线形设计之间的协调性,提高高速公路车辆运行的安全性,开展高速公路设计一致性评价研究.在收集实际高速公路线形及事故数据的基础上,应用交互式道路安全设计模型(IHSDM)进行设计一致性评价,将评价结果与实际事故数据进行对比,分析了该模型的适用性.针对该模型在中国高速公路应用中存在的不足进行了改进,并将设计一致性存在问题路段与线形设计存在不足路段进行对比分析,验证了改进模型的有效性.分析结果表明:改进模型评价结果准确率为83.3％,较原模型评价准确率提高了25％,研究成果可以为高速公路线形设计的安全性评价提供技术支持.