Influence of rainfall on skid resistance performance and driving safety conditions of asphalt pavements

To study the influence of rainfall on pavement skid-resistance performance and driving safety,the water film thickness(WFT)concept considering the longitudinal and transverse slopes of the pavement was utilized based on the total discharge formulation and turbulence theory of slope flow.Using experimental data measured using the British pendulum test under varying WFT levels,a model for calculating the skid resistance,namely the British pendulum number(BPN),was formulated and used to quantitatively evaluate the effects of rainfall intensity,transverse,and longitudinal slopes on the computed BPN.The study results reveal that skid resistance is linearly proportional to the pavement transverse slope and inversely proportional to the rainfall intensity and the pavement longitudinal slope.In particular,rainfall intensity,along with pavement texture depth,exhibited a significant impact on the tire-pavement friction and skid-resistance performance.The results further indicate that driving safety under wet weather is predominantly governed by skid resistance and visibility.The BPN and sideway force coefficient(SFC60)values for new asphalt pavements under different rainfall intensities are provided along with some modification to the stopping sight distance(SSD)criteria.Safe driving speed limits are also determined using a safe-driving model to develop the appropriate speed limit strategies.The overall study results provide some insights,methodology approach,and reference data for the evaluation of pavement skid-resistance performance and driving safety conditions under different pavement slopes and rainfall intensities.

Influence of Friction Drive Lift Gears Construction on the Length of Braking Distance

The friction drive elevators the influence of the braking distance has very high significance to meet certain safety regulations and comfort.During the emergency braking the delay for the system a frame and a cabin should be within the range from 0.2 to 9.81 m/s~2.However,there are no specialist literatures regarding the issues connected with emergency braking of elevating devices either.The results of the own empirical research work are presented regarding the influence of design changes on the working parameters of the friction drive elevator gears.ASG100,KB 160,PP16,PR2000UD and CHP2000 types of safety progressive gears are analyzed.ASG100,KB 160,PP16,PR2000UD type progressive gears are already produced by European manufacturers.CHP2000 type gears are established as the alternative option for the already existing solutions.The unique cam system has been used in the CHP 2000 gears.The cam leverage gives the chance to unblock,in a very easy way,the clamed gears after braking.Thus,it is a key aspect to perform laboratory tests over the braking process of a newly created solution.The proper value of the braking distance has a significant influence on the value of delay in terms of binding standards.The influence of loading on the effective braking distance and the value of the falling elevator cabin speed are analyzed and the results are presented.The results presented are interesting from lift devices operation and a new model of CHP 2000progressive gear point of view.

CAV driving safety monitoring and warning via V2X-based edge computing system

Driving safety and accident prevention are attracting increasing global interest.Current safety monitoring systems often face challenges such as limited spatiotemporal coverage and accuracy,leading to delays in alerting drivers about potential hazards.This study explores the use of edge computing for monitoring vehicle motion and issuing accident warnings,such as lane departures and vehicle collisions.Unlike traditional systems that depend on data from single vehicles,the cooperative vehicle-infrastructure system collects data directly from connected and automated vehicles(CAVs)via vehicle-to-everything communication.This approach facilitates a comprehensive assessment of each vehicle’s risk.We propose algorithms and specific data structures for evaluating accident risks associated with different CAVs.Furthermore,we examine the prerequisites for data accuracy and transmission delay to enhance the safety of CAV driving.The efficacy of this framework is validated through both simulated and real-world road tests,proving its utility in diverse driving conditions.

Driving Behavior Shaping Model in Road Traffic System

In order to give a new way for modeling driving behavior, identifying road traffic accident causation and solving a variety of road traffic safety problems such as driving errors prevention and driving behavior analysis, a new driving behavior shaping model is proposed, which could be used to assess the degree of effect of driving error upon road traffic safety. Driver behavior shaping model based on driving reliability and safety analysis could be used to identify the road traffic accident causation, to supply data for driver's behavior training, to evaluate driving procedures, to human factor design of road traffic system.

Towards the Unified Principles for Level 5 Autonomous Vehicles

The rapid advance of autonomous vehicles(AVs)has motivated new perspectives and potential challenges for existing modes of transportation.Currently,driving assistance systems of Level 3 and below have been widely produced,and several applications of Level 4 systems to specific situations have also been gradually developed.By improving the automation level and vehicle intelligence,these systems can be further advanced towards fully autonomous driving.However,general development concepts for Level 5 AVs remain unclear,and the existing methods employed in the development processes of Levels 0-4 have been mainly based on task-driven function development related to specific scenarios.Therefore,it is difficult to identify the problems encountered by high-level AVs.The essential logical and physical mechanisms of vehicles have hindered further progression towards Level 5 systems.By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving,we put forward a coordinated and balanced framework based on the brain-cerebellum-organ concept through reasoning and deduction.Based on a mixed mode relying on the crow inference and parrot imitation approach,we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning,self-adaptation,and self-transcendence for AVs.From a systematic,unified,and balanced point of view and based on least action principles and unified safety field concepts,we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs,specifically at Level 5.

Drivers’ EEG Responses to Different Distraction Tasks

Driver distraction has been deemed a major cause of traffic accidents.However,drivers’brain response activities to different distraction types have not been well investigated.The purpose of this study is to investigate the response of electroencephalography(EEG)activities to different distraction tasks.In the conducted simulation tests,three secondary tasks(i.e.,a clock task,a 2-back task,and a navigation task)are designed to induce different types of driver distractions.Twenty-four participants are recruited for the designed tests,and differences in drivers’brain response activities concerning distraction types are investigated.The results show that the differences in comprehensive distraction are more significant than that in single cognitive distraction.Friedman test and post hoc two-tailed Nemenyi test are conducted to further identify the differences in band activities among brain regions.The results show that the theta energy in the frontal lobe is significantly higher than that in other brain regions in distracted driving,whereas the alpha energy in the temporal lobe significantly decreases compared to other brain regions.These results provide theoretical references for the development of distraction detection systems based on EEG signals.

ROAD SAFETY DRIVE

China is embracing the expertise of international organizations and industries to raise awareness of road safety in a bid to slash road accident

Research on Superelevation Design in Highway Route Design

Superelevation design can basically be regarded as an important part of highway route design system.In the specific design process,designers need to strictly comply with the requirements of superelevation design specifications to ensure the driving stability of curved road section.However,due to the differences of road operation conditions in different regions of China,there are many uncertain factors in the design of super elevation,resulting in design errors.In order to reduce design errors as much as possible,this paper mainly based on the highway route design,highway route design problems and key measures are summarized for reference.

Micro-simulation insights into the safety and operational benefits of autonomous vehicles

Several past studies showed that Autonomous Vehicles(AVs)can reduce crash risk,stop-and-go traffic,and travel time.To analyze the safety benefits of AVs,most of the researchers proposed algorithms and simulation-based techniques.However,these studies have not assessed the safety benefits of AVs for different vehicle types under heterogeneous conditions.With this opportunity,this study focuses on the benefits of AVs in terms of safety for different penetration rates under heterogeneous conditions.This study considered three driving logics during peak hour conditions to assess the performance of AVs in terms of safety.In VISSIM,default driving behavior models for AVs were adopted to consider cautious and all-knowing driving logic and the third driving logic(Atkins)was modeled in VISSIM using parameters adopted from the previous studies.To this end,using VISSIM,the travel time output results were obtained.Also,using Surrogate Safety Assessment Model(SSAM),conflicts were extracted from output trajectory files(VISSIM).The results suggest that“cautious driving logic”reduced travel time and crash risk significantly when compared to the other two driving logics during peak hour conditions.Furthermore,the statistical analysis clearly demonstrated that“cautious driving logic”differs significantly from the other two driving logics.When Market Penetration Rates(MPR)were 50%or greater,the“cautious driving logic”significantly outperforms the other two driving logics.The results highlight that adopting“cautious driving logic”at an expressway may significantly increase safety at higher AV penetration rates(above 50%).

A dynamic speed guidance method at on-ramp merging areas of urban expressway considering driving styles

Dynamic speed guidance for vehicles in on-ramp merging zones is instrumental in alleviating traffic congestion on urban expressways.To enhance compliance with recommended speeds,the development of a dynamic speed-guidance mechanism that accounts for heterogeneity in human driving styles is pivotal.Utilizing intelligent connected technologies that provide real-time vehicular data in these merging locales,this study proposes such a guidance system.Initially,we integrate a multi-agent consensus algorithm into a multi-vehicle framework operating on both the mainline and the ramp,thereby facilitating harmonized speed and spacing strategies.Subsequently,we conduct an analysis of the behavioral traits inherent to drivers of varied styles to refine speed planning in a more efficient and reliable manner.Lastly,we investigate a closed-loop feedback approach for speed guidance that incorporates the driver’s execution rate,thereby enabling dynamic recalibration of advised speeds and ensuring fluid vehicular integration into the mainline.Empirical results substantiate that a dynamic speed guidance system incorporating driving styles offers effective support for human drivers in seamless mainline merging.

Driver Drowsiness Recognition Based on Computer Vision Technology

Driver drowsiness is one of the major causes of traffic accidents. This paper presents a nonintru- sive drowsiness recognition method using eye-tracking and image processing. A robust eye detection algo- rithm is introduced to address the problems caused by changes in illumination and driver posture. Six measures are calculated with percentage of eyelid closure, maximum closure duration, blink frequency, av- erage opening level of the eyes, opening velocity of the eyes, and closing velocity of the eyes. These meas- ures are combined using Fisher＇s linear discriminant functions using a stepwise method to reduce the cor- relations and extract an independent index. Results with six participants in driving simulator experiments demonstrate the feasibility of this video-based drowsiness recognition method that provided 86% accuracy.

Safety Analysis for Thread Pair Clearance of Safety Mechanism of Three Gorges Project Ship Lift

The Three Gorges Project（TGP） ship lift employs 4 safety mechanisms, of which one consists of a rotary locking screw and a nut jaw column. The thread pair clearance（TPC） of safety mechanisms is set at 60 mm. Owing to influential factors,the TPC changes randomly in the upward/downward-stroke of the ship chamber. If it diminished to 0, the safety mechanism would be jammed, thus resulting in disastrous accidents. By the bearing test of the drive system, 7 influential factors have been studied; 15 other influential factors（including 8 factors of manufacture and installation deviation, 3 factors of chamber offset, 2 factors of external load and 2 factors of wear） have been analyzed based on the design data. Results by the limit superposition reveal that the TPC change varies from -43.8 mm to ＋48.4 mm when the water level of the chamber ranges from 3.4 m to 3.6 m. According to the Gaussian distribution, the probability of the TPC change varied from -53.7 mm to ＋58.8 mm in the most detrimental status is99.74%, therefore, the TPC remains in a safe condition. This paper puts forward that two-phase operation of the drive system should be adopted so as to reduce the maximum TPC change to -44.6 mm.

Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle

Purpose–This study aims to evaluate the influence of connected and autonomous vehicle(CAV)merging algorithms on the driver behavior of human-driven vehicles on the mainline.Design/methodology/approach–Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing,which lacks consideration of realistic driving behaviors in the merging scenario.This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms.Findings–Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon.The results revealed significant variation of the algorithm influences.Specifically,the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles.Originality/value–To the best of the authors’knowledge,this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation.To achieve the research purpose,a novel multi-driver driving simulator was developed,which enables multi-drivers to simultaneously interact with each other during a virtual driving test.The results are expected to have practical implications for further improvement of the CAV merging algorithm.

Measurement and evaluation of whole body vibration of agricultural tractor operator

Exposure of whole body vibration(WBV)influences performance,comfort,and long term health risks of tractor operator.Therefore,measurement and evaluation of WBV parameters should be carried out to find probable effects on the health of tractor operators.In this study,a system was designed to measure the WBV of agricultural tractor operators and evaluated the hazard risks on operator’s body according to the ISO standards,and implementation of the WBV test in the official testing station was also suggested.A tri-axial accelerometer was employed to measure vibrations transmitted to the seated operator body as a whole through the supporting surface of the buttock on four typical farm roads under different speeds.The vector sum A(8)exposures on the rough tracks(earthen and grassland roads)exceeded the action limits of 0.5 m/s2 at a 10.9 km/h forward speed and reached to the action limit value at a 16.0 km/h forward speed on the concrete road.The vector sum of VDV(8)exposures did not exceed the action limits of 9.1 m/s1.75 and was greater on the grassland road.The vector sum Sed(8)exposures values exceeded the moderate probability of an adverse health limit of 0.5 MPa on all farm roads at high forward speeds and exceeded the high probability of an adverse health limit of 0.8 MPa on asphalt,concrete,and grassland roads which should be lower than the exposure limit values as suggested by the ISO and EC standards.The WBV evaluation procedure should be considered for implementation at the official tractor test station,which would response to domestic and international tractor test regulations and improve the market competitiveness.

Deep Reinforcement Learning Enabled Decision-Making for Autonomous Driving at Intersections

Road intersection is one of the most complex and accident-prone traffic scenarios,so it’s challenging for autonomous vehicles(AVs)to make safe and efficient decisions at the intersections.Most of the related studies focus on the solution to a single scenario or only guarantee safety without considering driving efficiency.To address these problems,this study proposed a deep reinforcement learning enabled decision-making framework for AVs to drive through intersections automatically,safely and efficiently.The mapping relationship between traffic images and vehicle operations was obtained by an end-to-end decision-making framework established by convolutional neural networks.Traffic images collected at two timesteps were used to calculate the relative velocity between vehicles.Markov decision process was employed to model the interaction between AVs and other vehicles,and the deep Q-network algorithm was utilized to obtain the optimal driving policy regarding safety and efficiency.To verify the effectiveness of the proposed decision-making framework,the top three accident-prone crossing path crash scenarios at intersections were simulated,when different initial vehicle states were adopted for better generalization capability.The results showed that the developed method could make AVs drive safely and efficiently through intersections in all of the tested scenarios.

Dangerous Driving Behavior Recognition and Prevention Using an Autoregressive Time-Series Model

Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.

Adaptive beaconing for collision avoidance and tracking accuracy in vehicular networks

In vehicular networks,the exchange of beacons among neighboring vehicles is a promising solution to guarantee a vehicle's safety.However,frequent beaconing under high vehicle density conditions will cause beacon collisions,which are harmful to a vehicle's driving safety and the location tracking accuracy.We propose an ABIwRC(Adaptive Beaconing Interval with Resource Coordination)method for a highway scenario.Each vehicle broadcasts beacon interval requests,including the intervals needed for both the vehicle's driving safety and location tracking accuracy.The RSU(Road Side Unit)allocates resources for a vehicle's beaconing according to the requests from all vehicles and the interference relationship between the vehicles in adjacent RSUs.We formulate a resource allocation problem for maximizing the sum utility,which measures the satisfaction of vehicles'requests.We then transform the optimization problem into a maximum weighted independent set problem,and propose an algorithm to solve this effciently.Simulation results show that the proposed method outperforms the benchmark in terms of beacon reception ratio,vehicle driving safety,and location tracking accuracy.

Opportunities and challenges of smart mobile applications in transportation

Smart mobile applications are software applications that are designed to run on smart phones, tablets, and other mobile electronic devices. In this era of rapid technological advances, these applications have become one of the primary tools we use daily both in our personal and professional lives. The applications play key roles in facilitating many applications that are pivotal in our today＇s society including communication, education, business, entertainment, medical, finance, travel, utilities, social, and transportation. This paper reviewed the opportunities and challenges of the applications related to transportation. The opportunities revealed include route planning, ridesharing/carpooling, traffic safety, parking information, transportation data collection, fuel emissions and consumption, and travel information. The potential users of these applications in the field of transportation include （I） transportation agencies for travel data collection, travel information, ridesharing/carpooling, and traffic safety, （2） engineering students for field data collection such as travel speed, travel time, and vehicle count, and （3） general traveling public for route planning, ridesharing/carpooling, parking, traffic safety, and travel information. Significant usage of smart mobile applications can be potentially very beneficial, particularly in automobile travel mode to reduce travel time, cost, and vehicle emissions. In the end this would make travel safer and living environments greener and healthier. However, road users＇ interactions with these applications could manually, visually, and cognitively divert their attention from the primary task of driving or walking. Distracted road users expose themselves and others to unsafe behavior than undistracted. Road safety education and awareness programs are vital to discourage the use of applications that stimulate unsafe driving/walking behaviors. Educating the traveling public about the dangers of unsafe driving/walking behavior could have significant safety benefits to all road users. Future research needs to compare accuracies of the applications and provide guidelines for selecting them for certain transportation related applications.