Changes In Road Environment Research Articles

Enhancing urban real-time PM2.5 monitoring in street canyons by machine learning and computer vision technology

During peak hours, both pedestrians and drivers face extended exposure to road air pollution, raising the risk of respiratory diseases. Variations in traffic volume, building attributes, and weather conditions in street canyons lead to differing exposure levels across road sections and intersections. Though deploying numerous sensors provides real-time access to PM2.5 levels in each road section, they cannot interpret the impact of weather, traffic, and buildings on these levels. Therefore, in Lanzhou City, we attempted to use Computer Vision Technology (CVT) to extract real-time traffic volume and street-view features from traffic images for PM2.5 concentration prediction, and to interpret the impact of road environment changes on PM2.5 levels. Results show that by using the Extreme Gradient Boosting (XGB) regression model, the coefficient of determination for PM2.5 prediction reaches R2=0.956. Meteorological conditions, traffic volume, and buildings are key variables in predicting road PM2.5 concentrations. Meteorological conditions control the continuous fluctuation of road PM2.5 levels, while traffic volume can lead to sudden changes in PM2.5 levels. The research indicates that the combination of traffic cameras and CVT can acquire road PM2.5 concentrations, contributing significantly to rapidly understanding road pollution status, identifying highly polluted roads, and conducting exposure assessments of roadways.

Research on Adaptive Cruise Systems Based on Adjacent Vehicle Trajectory Prediction

Vehicles in the adjacent lane making abrupt lane changes is a common and frequent action during traffic movement. Being aware of adjacent vehicles ahead of time, determining their cut-in intention, monitoring their cut-in trajectory in real time, and actively adjusting following speed are all critical for adaptive cruise systems for vehicles. This study proposes a flexible following-factor-calculation approach that considers the driver’s willingness to take risks for the purpose of identifying cut-in intent, predicting trajectory, and narrowing the window for following cruise speed adjustment to improve passenger ride comfort. To begin, a lane-change trajectory prediction algorithm based on driver adventitious factor correction is proposed in order to correctly predict the lane-change trajectory of adjacent vehicles in urban road traffic scenarios. Second, the flexible following factor and the flexible switching factor of the following target are constructed to overcome the influence of the uncertainty caused by internal and external disturbances on the vehicle following the motion process, and to reduce the impact of cut-in events on passenger comfort. An anti-disturbance rejection control and an adaptive cruise controller based on the vehicle’s longitudinal inverse dynamics model are proposed in order to compensate for and suppress the internal perturbations caused by the vehicle’s internal parameter changes and the random disturbances caused by external road environment changes. The results of simulation and real-world testing showed an average of 28% improvement in passenger comfort.

ORCLSim: A System Architecture for Studying Bicyclist and Pedestrian Physiological Behavior through Immersive Virtual Environments

Injuries and fatalities for vulnerable road users, especially bicyclists and pedestrians, are on the rise. To better inform design for vulnerable road users, we need to evaluate how bicyclist and pedestrian behavior and physiological states change in different roadway design and contextual settings. Previous research highlights the advantages of using immersive virtual environments (IVEs) in conducting bicyclist and pedestrian studies. These environments do not put participants at risk of injury, are low cost compared to on-road or naturalistic studies, and allow researchers to fully control variables of interest. In this paper, we propose a framework, Omni-Reality and Cognition Lab Simulator (ORCLSim), to support human sensing techniques within IVEs to evaluate bicyclist and pedestrian physiological and behavioral changes in different contextual settings. To showcase this framework, we present two case studies, where pilot data from five participants’ physiological and behavioral responses in an IVE setting are collected and analyzed, representing real-world roadway segments and traffic conditions. Results from these case studies indicate that physiological data are sensitive to road environment changes and real-time events in the IVE, especially changes in heart rate and gaze behavior. In addition, our preliminary data indicate participants may respond differently to various roadway settings (e.g., signalized vs. unsignalized intersections). By analyzing these changes, future studies can identify how participants’ stress level and cognitive load are impacted by the surrounding environment. The ORCLSim system architecture is a prototype that can be customized for future studies in understanding users’ behavioral and physiological responses in virtual reality settings.

Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes

LiDAR-based localization has been widely used for the pose estimation of autonomous vehicles. Since the localization requires a sustainable map reflecting environment changes, a map update framework based on crowd-sourcing measurements has been researched. Unfortunately, a point cloud map occupies too large data size to transmit data in the uploading and downloading of the map update framework. To realize the LiDAR map update framework by reducing the data size, we proposed a novel map update framework using a Geodetic Normal Distribution (GND) map that compresses the point cloud to the normal distributions. The proposed GND map update framework comprises two parts: map change detection based on crowd-sourcing vehicles and map updating based on a map cloud server. GND map changes are detected based on an evidence theory considering geometric relationships between the GND map and crowd-sourcing measurements and uploaded to the map cloud server. Uploaded map changes reproduce representative map changes based on a similarity-based clustering, which are updated into the GND map. The proposed framework was evaluated in simulations and real environments on construction sites. As a result, although partial map changes occurred, the GND map was kept up-to-date through the proposed framework and the localization for autonomous driving was performed successfully.

Agresivní řízení a jeho projevy ve vztahu k rysům osobnosti, dalším osobnostním proměnným, situačnímu kontextu a možná preventivní opatření

Aggressive driving is an important phenomenon today. Our study describes aggressive driving, its specific manifestation, and analyses possible causes and the main context. It´s shown that aggressive driving is a complicated construct with multifactorial causes and has a wide range of manifestations, from mild forms to road rage. The main point of aggressive driving is such driver´s behaviour, which threatens or restricts other road users and leads to risky situations. Lots of studies find as the main reason for aggressive driving the personality characteristics (sensation-seeking, impulsivity, aggressiveness, and irritability), the personality structure Big Five seems to be a good predictor seems. Other studies rate as the key causes the situational factors, like traffic congestion, anonymity on the roads, and time pressure. The multifactorial approach evaluates this phenomenon as the result of internal and external factors. The important influencing factors are social norms and driving culture in a country. In conclusion, we discuss possible preventive measures for minimization of aggressive driving behaviour that should be complex and effect as individuals (therapeutic programs for aggressive drivers, education in driving schools preventive campaigns) so external environment (changes in road environment with the goal of minimization the driving pressure).

Updating Point Cloud Layer of High Definition (HD) Map Based on Crowd-Sourcing of Multiple Vehicles Installed LiDAR

A high-definition (HD) map is becoming an integral component of future mobility systems such as autonomous and connected vehicles. Advances in computing systems, LiDAR technologies, and vehicle communication technologies have enabled the HD map to directly treat a point cloud map (PCM), modeling road environments as LiDAR signal-level data. However, if actual road environments are changed, the PCM, modeling the environments before changes, can not be used for vehicle applications. Accordingly, the PCM has to stay up-to-date states by reflecting the environment changes continuously. This paper presents a crowd-sourcing framework to update the PCM from environment changes continuously using LiDAR and vehicle communication. Multiple intelligent vehicles installed with the LiDAR sensors download the PCM from a map server via wireless vehicle communication. To minimize the effects of environment changes, a robust localization based on a hierarchical Simultaneous Localization and Mapping (SLAM) estimates the pose (position and direction). The estimated pose is used to detect the differences between the PCM and environments, which are defined as map changes. The map changes are detected by the probabilistic and evidential theory considering the LiDAR characteristics, such as beam divergence and multi-echo. The detected map changes are uploaded to the map cloud server and merged into the PCM. The proposed crowd-sourcing framework to keep the PCM up-to-date is verified and evaluated via simulations and experiments in sites with road environment changes.

Orclsim: A System Architecture for Studying Bicyclist and Pedestrian Physiological Behavior Through Immersive Virtual Environments

Injuries and fatalities for vulnerable road users, especially bicyclists and pedestrians, are on the rise. To better inform design for vulnerable road users, we need to conduct more studies to evaluate how bicyclist and pedestrian behavior and physiological states change in different roadway designs and contextual settings. Previous research highlights the advantages of Immersive Virtual Environment (IVE) in conducting bicyclist and pedestrian studies. These environments do not put participants at risk of getting injured, are low-cost compared to on-road or naturalistic studies and allow researchers to fully control variables of interest. In this paper, we propose a framework ORCLSim, to support human sensing techniques within IVE to evaluate bicyclist and pedestrian physiological and behavioral changes in different contextual settings. To showcase this framework, we present two case studies where we collect and analyze pilot data from five participants' physiological and behavioral responses in an IVE setting, representing real-world roadway segments and traffic conditions. Results from these case studies indicate that physiological data is sensitive to road environment changes and real-time events, especially changes in heart rate and gaze behavior. Additionally, our preliminary data indicates participants may respond differently to various roadway settings (e.g., intersections with or without traffic signal). By analyzing these changes, we can identify how participants' stress levels and cognitive load is impacted by the simulated surrounding environment. The ORCLSim system architecture can be further utilized for future studies in users' behavioral and physiological responses in different virtual reality settings.

Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural Network

In this study, we propose a method to recognize road environments with automotive frequency-modulated continuous wave (FMCW) radar systems. For automotive radar systems on the road, diverse road environments are observed. Each road environment generates unnecessary echoes known as clutter, and the magnitude distribution of received radar signal varies depending on road structures. Therefore, it is necessary to classify the road environment and adopt a target detection algorithm suitable for each road environment characteristic. To recognize the road environment in advance, it is necessary to identify the section where the road environment changes. In this paper, we define a changed road area as a transition region, and we classify the road environment and transition regions to improve the road environment recognition performance. Road environments are recognized by applying convolutional neural networks to the frequency-domain received signals of 77 GHz FMCW automotive radar systems. Experimental results in real-road environments demonstrate that the proposed method achieves 100% recognition performance, which is better if compared with that of the conventional methods.

Mental workload is reflected in driver behaviour, physiology, eye movements and prefrontal cortex activation

Mental workload is an important factor during driving, as both high and low levels may result in driver error. This research examined the mental workload of drivers caused by changes in road environment and how such changes impact upon behaviour, physiological responses, eye movements and brain activity. The experiment used functional near infrared spectroscopy to record prefrontal cortex activation associated with changes in mental workload during simulated driving. Increases in subjective ratings of mental workload caused by changes in road type were accompanied by increases in skin conductance, acceleration signatures and horizontal spread of search. Such changes were also associated with increases in the concentration of oxygenated haemoglobin in the prefrontal cortex. Mental workload fluctuates during driving. Such changes can be identified using a range of measures which could be used to inform the development of in-vehicle devices and partially autonomous systems.

Development of a Change Detection Method with Low-Performance Point Cloud Data for Updating Three-Dimensional Road Maps

Three-dimensional (3D) road maps have garnered significant attention recently because of applications such as autonomous driving. For 3D road maps to remain accurate and up-to-date, an appropriate updating method is crucial. However, there are currently no updating methods with both satisfactorily high frequency and accuracy. An effective strategy would be to frequently acquire point clouds from regular vehicles, and then take detailed measurements only where necessary. However, there are three challenges when using data from regular vehicles. First, the accuracy and density of the points are comparatively low. Second, the measurement ranges vary for different measurements. Third, tentative changes such as pedestrians must be discriminated from real changes. The method proposed in this paper consists of registration and change detection methods. We first prepare the synthetic data obtained from regular vehicles using mobile mapping system data as a base reference. We then apply our proposed change detection method, in which the occupancy grid method is integrated with Dempster–Shafer theory to deal with occlusions and tentative changes. The results show that the proposed method can detect road environment changes, and it is easy to find changed parts through visualization. The work contributes towards sustainable updates and applications of 3D road maps.

2P1-R08 Mobile Mapping Systemによる道路視点からの東日本大震災被災地域調査 : 時系列三次元点群比較による道路周辺環境の変化域抽出(ITSとロボット・メカトロ技術)

2P1-R08 Mobile Mapping Systemによる道路視点からの東日本大震災被災地域調査 : 時系列三次元点群比較による道路周辺環境の変化域抽出(ITSとロボット・メカトロ技術)

Coroners' recommendations following fatal heavy vehicle crash investigations

This paper quantifies and describes the nature of coroners' recommendations and comments on fatal heavy vehicle crashes in Victoria, Australia. A retrospective, descriptive study was performed using coroners' findings. Fatal heavy vehicle crashes between January 2001 and December 2007 were identified through coronial databases. Individual findings were examined by incident type. Identified recommendations or preventative comments were reviewed and compared with national heavy vehicle safety objectives. Of 330 fatal crashes, which resulted in 376 deaths, recommendations were made in 21 incidents (6%). From these 21 incidents, 45 separate recommendations or comments were made by coroners. Ten (22%) called for specific remedial action, predominantly targeting road environment changes; the remainder had a broader application for prevention. Of the 21 incidents from which these recommendations arose, 11 (52%) were from a public inquest. No recommendation was made following any of the 45 single heavy vehicle crashes. The frequency of coroners' recommendations varied by crash nature including vehicle type involved and number of resulting fatalities. Multiple factors are likely to influence their development, including the holding of a public inquest and the perceived level of preventability. Coroners' investigations serve an important public health and safety role. Recognition of the significance of recommendations for reducing the extent of injury from heavy vehicle crashes and monitoring of their uptake is vital.

Fatigued driver’s driving behavior and cognitive task performance: Effects of road environments and road environment changes

This study aims to explore the effects of different road environments and their changes on driving behaviors and cognitive task performance of fatigued drivers. Twenty-four participants volunteered in a 2 (road environment)×3 (fatigue level) within-subjects factorial design simulated driving experiment. Participants were asked to perform basic numerical calculation and distance estimation of traffic signs when driving normally, and provide answers to a questionnaire on fatigue rating. Results show that fatigued drivers faced greater attention demand, were less alert, and tended to overestimate the distance to roadside traffic signs. Fatigue caused by driving in complex road environment had the greatest negative impact on driving behavior and visual distance estimation, and the fatigue transfer effect worsened significantly but differently on both driving behavior and performance of fatigued drivers when switching from a complex to a monotonous road environment and vice versa. Notably, this study shows that fatigued drivers performed relatively better in arithmetic tasks than non-fatigued ones. In addition, when switching from a monotonous to a complex road environment, drivers’ performance in visual distance estimation and arithmetic tasks improved though their driving behavior deteriorated, revealing that the fatigue effect upon drivers might be explained to some extent by their alertness and arousal levels.

Do familiarity and expectations change perception? Drivers’ glances and response to changes

The present study shows that repeated exposure to a road environment changes eye movement behaviour. In addition, repeated exposure may result in inadequate responses to unexpected changes in the road environment. Participants drove a low-cost simulator while their eye movements were recorded. With repeated exposure participants’ glances at traffic signs along the route were shorter while having a better recollection of the traffic signs along the route. At the last drive, the priority situation at an intersection was changed (a priority road was changed into a yield situation). Even though drivers glanced at the sign that indicated the new priority situation, they did not sufficiently process the information to show an adequate response. Only two out of 12 drivers showed any response, being a response only after crossing the priority road markings. The current finding that unexpected but relevant information may be missed by drivers is relevant for other monitoring tasks.

지능형 교통시스템을 위한 자동차 추적에 관한 연구

본 논문은 영상검지기를 이용하여 도로상에서 주행하는 차량의 움직임 추적 시스템에 필요한 탐지방법과 이동궤적을 추적하는 방법을 제안하여 차량의 움직임을 추적하는 시스템을 구현하였다. 도로상에서 주행하는 차량의 움직임을 측정하는 이유는 지능형 교통 시스템의 첨단교통관제에 필요한 정보를 제공한 수 있으며, 기존에 설치되어진 매설식 루프 검지기가 가지는 유지보수의 문제를 해결할 수 있다. 본 논문에서는 양방향 도로에서 주행하는 차량의 물체 탐지를 위하여 차영상 분석법을 기반으로 하였다. 이는 도로의 주변 환경이 빠르게 변화하기 때문에 배경영상을 사용하는 방법은 적합하지 않기 때문이다. 본 논문에서는 칼만필터와 이노베이션을 사용한 가변 탐색영역으로 차량의 이동 궤적을 추적하였다. 가변 탐색영역을 사용한 이유는 기존에 제한된 검색영역을 이용한 방법에서 나타나 질 수 있는 차량의 이동 속도 및 궤적의 변화에 따른 문제를 해결 할 수 있기 때문이다. 실험 결과 제한된 검색영역을 사용하는 방식보다 제안하는 방법이 우수한 성능을 보임을 알 수 있었다. In this paper, we propose a method about the extraction of vehicle and tracking trajectory for moving vehicle tracking system in road. This system applied to the monitoring system of the traffic flow for ATMS(advanced traffic management system) of ITS(intelligent transport system). Also, this system can solve the problem of maintenance of loop sensor. And we detected vehicle using differential image analysis. Because of the road environment changes by real time. Therefore, the method to use background image is not suitable. And we used Kalman filter and innovation value and variable search area for vehicle tracking system. Previous method using fixed search area is sensitive to the moving trajectory and the speed of vehicle. Simulation results show that proposed method increases the possibility of traffic measurement more than fixed area traffic measurement system.