Road Center Research Articles

A dataset of the 1 km-grid road network density in China (2019)

Road network density is a basic index of evaluating regional road development scale and road network development degree. The measurement and monitoring of road network density (as a comprehensive characterization index) plays an important role in the assessment of regional traffic and economic development, and provide reference for urban road management and control measures, which is of great significance to urban planning and municipal management. In this paper, based on the vector data of railway land, highway land, urban and village road land and rail transit in the Third National Land Survey, we extracted the road center line, using the km-grade network production software, to obtain a dataset of the 1km-grid road network density in China in 2019. Rigorous quality control and quality inspection measures throughout all stages of data processing have been implemented, including road surface vector extraction, road skeleton line extraction, and kilometer grid density calculation, to ensure the correctness, integrity and consistency of the data. This dataset can be used for the study on the preparation for the national territory development plan, the evaluation of plan implementation, the calculation of status factors for regional transportation development, and accessibility calculation.

Text and Voice Message Distraction Detection: A Machine Learning Approach Using Vehicle Trajectory Data

Cellphone usage is often considered to be one of the major causes of distracted driving. Similarly, voice messaging is identified as a potential cause of distracted driving but has received limited attention in the literature. Thus, this study aims to develop supervised machine learning (ML) methods to detect distracted driving events caused by texting and voice messaging using vehicle trajectory data. Vehicle trajectory data was collected from 92 participants who drove a simulated network of the Baltimore metropolitan area using a driving simulator. Different key variables were extracted from the data to construct the features for developing the ML methods, including speed, brake usage, throttle, steering velocity, brake light, and offset from the road center. Several methods, including the support vector machine, k-nearest neighbor, decision tree, neural network, and adaptive boosting (AdaBoost), were examined on the data to achieve the best model. In addition, several metrics were used to assess the performance of the ML models, such as accuracy, sensitivity, precision, the Matthews correlation coefficient, and the area under the receiver operating characteristic curve (AUC-ROC). The results indicated that the AdaBoost algorithm had the best yields, with an accuracy of 74.67% and AUC-ROC of 82.5% on the independent test set. The findings of this study can be directly leveraged to develop in-vehicle driver warning systems to alert drivers with respect to distracted behavior, which lead to the reduction of distracted driving events and an improvement in traffic safety.

Basement structural design innovation of China International Silk Road Center

SummaryFor super high‐rise buildings with a height of more than 400 m, differential settlement control of foundation is a crucial aspect in structural design. In previous engineering cases, except that the foundation or piles can be directly supported on bedrock, basement wing walls were commonly used to coordinate the differential settlement of foundation between the perimeter columns and the core. However, wing walls could also have negative effects on the basement usage. This article studied and compared the effects of arranging basement wing walls and adjusting pile length on differential settlement of the foundation. The scheme of adjusting pile length considering the interaction between piles, foundation, and superstructure was proposed to control differential settlement of the foundation, and the effectiveness of canceling basement wing walls was verified. Subsequently, a calculation program was developed to automatically optimize pile lengths by region. Finally, the above achievements were applied to the project of China International Silk Road Center Building with a building height of 498 m, making it the tallest building in China located on nonrock stratum and without basement wing walls.

RESULT OF ARCHAEOLOGICAL RESEARCH WORK, CONDUCTED IN THE ANCIENT SETTLEMENT OF KULTOBE IN 2023

It is known that the history of Keles dates back to the Stone Age. The fact that mankind has lived in this region since the Stone Age is evidenced by the sites, located on the slopes of the Kazygurt mountains. Judging by the numerous settlements, towns and burials found in river valleys with a lot of water, it is clear that the region has become a fertile place for humanity since the Early Iron Age. As a result of archaeological research was revealed that the ancient peoples who lived in the Keles steppe were engaged in agriculture, as well as nomadic cattle breeding, even built cities and formed the first urban culture in the region. In the Middle Ages, the Keles plain became an important center of the Great Silk Road, which connected the settlements of Sairam and Shymkent with the city of Shash and from the city of Shash with the cities of Baiyrkum, Sutkent, Otyrar and Yassy. Thus, the trade and art, agriculture and animal husbandry of the settlements of the Keles plain developed simultaneously and became the basis for the formation of urban culture. In the article, we decided to study, analyze and introduce into scientific circulation the topographic structure of the settlement of Kultobe, the architectural features of residential buildings and the defence system, which has not been fully studied. The article was prepared on the basis of the scientific project AP19678134 «Carrying out archaeological and interdisciplinary research on the ancient settlement of Kultobe located in the Keles region».

Pastoral Sociology and Development Paradigm in Nepal

The main objective of this study is to dissect development from the prospective of pastoral sociology. It's grounded on secondary data, numerous exploration papers, sociological occasional journals, library and demonstrated accoutrements, secondary information sources have formerly been used. Pastoral Sociology plays a vital part to understand the problems and results of pastoral society especially for the developing country like Nepal. The study reveals that because of the significance of pastoral sociology the literacy and tutoring of sociology has been started in Nepal. Now in present days, institute of husbandry, institute of forestry, irrigation systems etc has been enforced in the name of pastoral sociology. There is the great part of pastoral sociologist in poverty elevation fund, planning commission, pastoral megacity, department of road and original development training center. The policy makers, political leaders, development workers should give emphasis on the literacy, tutoring and new inquiries on pastoral sociology with connecting the development. Teaching the farmers about the products appropriate in the particular farms based on the quality of soil across Nepal can help the country to develop economically and ultimately it leads every concerned one to become independent financially by exporting the local products to the most promising markets at home and beyond. The possibility of commercial agriculture can amplify the possibility of economic growth. Therefore, the interconnection between these aspects must be comprehensively understood and for this government of Nepal must take initiatives.

Snow accumulation, albedo and melt patterns following road construction on permafrost, Inuvik–Tuktoyaktuk Highway, Canada

Abstract. Roads constructed on permafrost can have a significant impact on the surrounding environment, potentially inducing permafrost degradation. These impacts arise from factors such as snow accumulation near the road, which affects the soil's thermal and hydrological regime, and road dust that decreases the snow's albedo, altering the timing of snowmelt. However, our current understanding of the magnitude and the spatial extent of these effects is limited. In this study we addressed this gap by using remote sensing techniques to assess the spatial effect of the Inuvik to Tuktoyaktuk Highway (ITH) in Northwest Territories, Canada, on snow accumulation, snow albedo and snowmelt patterns. With a new, high resolution snow depth raster from airborne laser scanning, we quantified the snow accumulation at road segments in the Trail Valley Creek area using digital elevation model differencing. We found increased snow accumulation up to 36 m from the road center. The magnitude of this snow accumulation was influenced by the prevailing wind direction and the embankment height. Furthermore, by analyzing 43 Sentinel-2 satellite images between February and May 2020, we observed reduced snow albedo values within 500 m of the road, resulting in a 12-days-earlier onset of snowmelt within 100 m from the road. We examined snowmelt patterns before, during and after the road construction using the normalized difference snow index from Landsat-7 and Landsat-8 imagery. Our analysis revealed that the road affected the snowmelt pattern up to 600 m from the road, even in areas which appeared undisturbed. In summary, our study improves our understanding of the spatial impact of gravel roads on permafrost due to enhanced snow accumulation, reduced snow albedo and earlier snowmelt. Our study underscores the important contribution that remote sensing can provide to improve our understanding of the effects of infrastructure development on permafrost environments.

Investigating the relationship between organizational intelligence and organizational agility on productivity in the road, housing and urban development research center of Tehran province

Background and purpose: The low level of employee productivity in some organizations is one of the problems faced by managers. Therefore, the current research was conducted with the aim of investigating the relationship between organizational intelligence and organizational agility on productivity in the Road, Housing and Urban Development Research Center of Tehran Province. Methodology: The current research was a correlational descriptive study. The statistical population was all employees of Tehran Road, Housing and Urban Development Research Center. Sampling was done by simple random sampling by preparing a list of 200 sample employees, and Albrecht (2002) organizational intelligence questionnaires, Sharifi and Chang (2004) organizational agility, and Hersi and Goldsmith (2000) productivity questionnaires were implemented. Findings: The results showed that there is a significant relationship between organizational intelligence and organizational agility with productivity. Further, the results of this research showed that organizational intelligence and strategic dimensions, morale, application of knowledge and performance pressure component as well as organizational agility and the two dimensions of competence and responsiveness were able to predict organizational productivity. Conclusion: The findings of the study showed that by increasing the intelligence of the organization, it is possible to provide the fields of the agile organization and bring the organization to its goals faster.

Driver behaviors assisted by different human machine interfaces to avoid rear-end collisions during level 2 automated driving

Objective To practically apply level 2 automated driving in complex traffic conditions, it is necessary to prompt driver behaviors to prevent potential accidents in areas where manual interventions are frequently required. Methods A driving simulator experiment with 20 participants was conducted to evaluate the impact of different human machine interfaces (HMIs) on drivers’ interventions in terms of braking to avoid rear-end collisions during level 2 automated driving when a motorcycle abruptly cut in near intersections. Two types of HMIs were tested: a static HMI that informed drivers about approaching intersections, and a sensor HMI that displayed real-time object recognition results. Each driver participated in five experimental conditions, which varied the presence or absence of the static and sensor HMIs during level 2 automated driving, with manual driving serving as the baseline condition. Results The maximum deceleration in terms of braking to avoid rear-end collisions was significantly larger when level 2 automated driving was used without any HMI, compared to that of manual driving. However, when the sensor HMI was applied together with the static HMI during level 2 automated driving, a comparable time to collision could be achieved with a significantly smaller deceleration, compared to that without any HMI. Drivers’ eye-gaze behaviors revealed that no significant difference existed in the percentages of gaze to the road center area, indicating that they were not distracted by the HMIs. Finally, drivers’ attention levels to surrounding traffic and feeling of safety were significantly higher when level 2 automated driving was used in combination with the static and sensor HMIs. Conclusions The results demonstrated that the combination of static and sensor HMIs successfully aided drivers in ensuring driving safety with a significantly smaller deceleration to avoid rear-end collisions during level 2 automated driving. Furthermore, drivers’ attention levels were maintained, and their feeling of safety was improved when both HMIs were used in combination.

The influence of alcohol and automation on drivers’ visual behavior during test track driving

BackgroundDriving under the influence of alcohol severely increases crash risk. Impairment detection during driving is therefore key to improve traffic safety. However, future detection systems need to capture impairment for more reasons than alcohol intoxication and must also function in all driving modes (manual, assisted, autonomous). Driver Monitoring Systems (DMS) are promising candidates for such broader impairment detection. MethodA test track study investigated the effects of alcohol intoxication on drivers’ visual behavior both when just driving and when engaged in a non-driving related task. Twenty-six participants performed two drives: 1) sober baseline, 2) with a target blood alcohol concentration of 0.1%. The participants drove in either manual, assisted, or autonomous drive (AD) mode. ResultsIntoxication influenced glance behavior in all driving modes. It was most evident during visually demanding non-driving related tasks where it resulted in longer single and total off-path glance durations. Additionally, when just driving in manual mode, almost one third of the drivers displayed gaze concentration to the forward roadway when intoxicated. For sober driving, the difference in visual behavior between manual and assisted mode were moderate. In contrast, there was a huge shift towards longer off-path glances, lower percent road center, and lower off-path glance frequency in AD mode. ConclusionsIntoxication clearly affects drivers’ on/off road glance behavior. However, it is necessary to account for both driving mode and engagement in non-driving related tasks to reliably distinguish sober from drunk driving. Glance metrics has the potential to serve as a sub-set of indicators for a broader DMS-based detection of impaired driving, which can inform the decisions on when to activate in-vehicle countermeasures.

Joint project of KNUCA and Zhejiang Yuexin Inspection Technology Co., Ltd.

Every year, international scientific conferences are held in China, dedicated to the search for high-class talents, the attraction of innovative technologies, the creation of joint ventures in various fields of science and production. One of the organizers of such events was the Ukrainian-Chinese Silk Road Center, which, as part of the "One Belt, One Road" program, regularly conducts online video road shows between Chinese and Ukrainian specialists. This time, projects from several branches of production, including mechanical engineering and robotics, were presented. The best works are selected for the preparation of grant applications for research funding in laboratories and research groups in China, as well as for participation in the country's production activities.
 One of the successful applications was a joint project of the Kyiv National University of Civil Engineering and Architecture and the pipeline diagnostics company Zhejiang Yuexin Inspection Technology Co., Ltd. Doctor of Technical Sciences, Professor M.K. Sukach took part in it as the technical director of the project., specialist in deep-sea technology. The Chinese side was headed by the project's chief manager, company director Jin Hangfei. The project became one of those selected by the Ukrainian-Chinese Center and nominated for a state grant. He won the support of the Beijing High-Tech Exchange and government funding for joint research. In addition to the scientific component, it provides an educational program for student training, PhD training and advanced training of specialists in the relevant field.

Electric vehicle entire-trip navigation and charging reservation method based on a high-speed communication network

Having a large number of electric vehicles (EVs) on the road will lead to vehicle congestion, long waiting times at charging stations (CSs), and even the inability to find an available charging position. Main technical challenges in mobile EVs charging may include charging information exchanges between EVs and CSs, EV traveling route and CS selection, and EV charging planning. To solve the above problems, this study provides an optimization solution for entire-trip charging and navigation of EVs, which balances charging resources, protects the travel time of EV drivers, and prioritizes renewable energy. The communication scheme of an EV charging network is designed, including real-time communication between the CSs, EV drivers, road condition monitoring center, power grid company, and the charging control center distributed in every parking lot. CSs in each road segment sort vehicles arriving at stations, allocate charging time slots, provide charging start times, and adjust those times for EVs with reservations that have not yet arrived. EV drivers can plan vehicle charging on their smart devices. The optimization of electric navigation can reduce the travel time or cost for drivers, ensure safe electricity usage, calculate vehicle routes, select CSs, and provide charging time intervals. The effectiveness of the method is verified on a regional road network in China, and the results show that it can balance the charging resource distribution of a CS, guarantee EV state of charge level, greatly reduce travel cost or travel time, provide precise waiting time, and prioritize renewable energy.

Performance Analysis of Real-Time GPS/Galileo Precise Point Positioning Integrated with Inertial Navigation System

The integration of global navigation satellite system (GNSS) precise point positioning (PPP) and inertial navigation system (INS) is widely used in navigation for its robustness and resilience, especially in case of GNSS signal blockage. With GNSS modernization, a variety of PPP models have been developed and studied, which has also led to various PPP/INS integration methods. In this study, we investigated the performance of a real-time GPS/Galileo zero-difference ionosphere-free (IF) PPP/INS integration with the application of uncombined bias products. This uncombined bias correction was independent of PPP modeling on the user side and also enabled carrier phase ambiguity resolution (AR). CNES (Centre National d'Etudes Spatiales) real-time orbit, clock, and uncombined bias products were used. Six positioning modes were evaluated, including PPP, PPP/INS loosely coupled integration (LCI), PPP/INS tightly coupled integration (TCI), and three of these with uncombined bias correction through a train positioning test in an open sky environment and two van positioning tests at a complex road and city center. All of the tests used a tactical-grade inertial measurement unit (IMU). In the train test, we found that ambiguity-float PPP had almost identical performance with LCI and TCI, which reached an accuracy of 8.5, 5.7, and 4.9 cm in the north (N), east (E) and up (U) direction, respectively. After AR, significant improvements on the east error component were achieved, which were 47%, 40%, and 38% for PPP-AR, PPP-AR/INS LCI, and PPP-AR/INS TCI, respectively. In the van tests, frequent signal interruptions due to bridges, vegetation, and city canyons make the IF AR difficult. TCI achieved the highest accuracies, which were 32, 29, and 41 cm for the N/E/U component, respectively, and also effectively eliminated the solution re-convergence in PPP.

Application of artificial solidified crust – PVDs method in ground improvement: A case study

Aiming at the deep soft soil area in transportation engineering where the ground cannot bear mechanical load, a new ground improvement method using artificial solidified crust (ASC) combined with prefabricated vertical drains (PVDs) was proposed. Based on a ground improvement project of using ASC-PVDs method in Shantou City, the construction process was described in detail, and the pore water pressure, settlement and lateral displacement of the deep soft ground were measured during embankment filling and preloading processes. The monitoring results show the ASC-PVDs method is suitable for deep soft ground improvement. After 302 days, the consolidation period was basically finished; the measured settlement of road center was − 1.526 m, and the maximum lateral displacement of the slop toe was 453 mm (at the depth of − 5 m). ASC reduced the settlement by about 20 %, and affected by it, the shallower soft soil bears more compressive deformation and lateral deformation.

Drivers’ gaze patterns when resuming control with a head-up-display: Effects of automation level and time budget

The removal of drivers’ active engagement in driving tasks can lead to erratic gaze patterns in SAE Level 2 (L2) and Level 3 (L3) automation, which has been linked to their subsequential degraded take-over performance. To further address how changes in gaze patterns evolve during the take-over phase, and whether they are influenced by the take-over urgency and the location of the human-machine interface, this driving simulator study used a head-up display (HUD) to relay information about the automation status and conducted take-over driving experiments where the ego car was about to exit the highway with variations in the automation level (L2, L3) and time budget (2 s, 6 s). In L2 automation, drivers were required to monitor the environment, while in L3, they were engaged with a visual non-driving related task. Manual driving was also embodied in the experiments as the baseline. Results showed that, compared to manual driving, drivers in L2 automation focused more on the HUD and Far Road (roadway beyond 2 s time headway ahead), and less on the Near Road (roadway within 2 s time headway ahead); while in L3, drivers’ attention was predominantly allocated on the non-driving related task. After receiving take-over requests (TORs), there was a gradual diversion of attention from the Far Road to the Near Road in L2 take-overs. This trend changed nearly in proportion to the time within the time budget and it exaggerated given a shorter time budget of 2 s. While in L3, drivers’ gaze distribution was similar in the early stage of take-overs for both time budget conditions (2 s vs. 6 s), where they prioritized their early glances to Near Road with a gradual increase in attention towards Far Road. The HUD used in the present study showed the potential to maintain drivers’ attention around the road center during automation and to encourage drivers to glance the road earlier after TORs by reducing glances to the instrument cluster, which might be of significance to take-over safety. These findings were discussed based on an extended conceptual gaze control model, which advances our understanding of gaze patterns around control transitions and their underlying gaze control causations. Implications can be contributed to the design of autonomous vehicles to facilitate the transition of control by guiding drivers’ attention appropriately according to drivers’ attentional state and the take-over urgency.

OpenStreetMap-Based Autonomous Navigation With LiDAR Naive-Valley-Path Obstacle Avoidance

OpenStreetMaps (OSM) is currently studied as the environment representation for autonomous navigation. It provides advantages such as global consistency, a heavy-less map construction process, and a wide variety of road information publicly available. However, the location of this information is usually not very accurate locally. In this paper, we present a complete autonomous navigation pipeline using OSM information as environment representation for global planning. To avoid the flaw of local low-accuracy, we offer the novel LiDAR-based Naive-Valley-Path (NVP) method that exploits the concept of "valley" areas to infer the local path always furthest from obstacles. This behavior allows navigation always through the center of trafficable areas following the road's shape independently of OSM error. Furthermore, NVP is a naive method that is highly sample-time-efficient. This time efficiency also enables obstacle avoidance, even for dynamic objects. We demonstrate the system's robustness in our research platform BLUE, driving autonomously across the University of Alicante Scientific Park for more than 20 km with 0.24 meters of average error against the road's center with a 19.8 ms of average sample time. Our vehicle avoids static obstacles in the road and even dynamic ones, such as vehicles and pedestrians.

Research on Urban Road Network Extraction Based on Web Map API Hierarchical Rasterization and Improved Thinning Algorithm

Most existing research on the vector road network is based on GPS trajectory travel information extraction, and urban GPS trajectory data are large and difficult to obtain. Based on this, this study proposes a road network extraction method based on network map API and designs a vector road network based on an improved image-processing algorithm using trajectory data. Firstly, a large number of trajectory data are processed by hierarchical rasterization. The trajectory points of the regional OD matrix are obtained by using the map API interface to generate the trajectory. Then, the image expansion processing is performed on the road network raster image to complete the information loss problem. The improved Zhang–Suen refinement algorithm is used to refine the idea to obtain the road center line, and the vector road network in the study area is obtained. Finally, taking the Harbin City of Heilongjiang Province as an example, compared with the road network of the network map, it has been demonstrated that using this technology may improve the traveler experience and the sustainability of urban traffic flow while reducing the number of manual procedures required, performing online incremental rapid change detection, and updating the present road network at a cheaper cost.

Cyber Traffic Light: Safe Cooperation for Autonomous Vehicles at Dynamic Intersections

Autonomous driving systems are becoming increasingly feasible and highly expected to be the heart of intelligent transportation systems. To deploy the autonomous driving vehicles on public roads, one of the practical challenges might be safe cooperation and collaboration among multiple vehicles, in particular when conflicts arise on shared road segments, such as road intersections, merge points, construction zones, single-track lanes, and center turn lane. In the current traffic systems, human drivers navigate these regions using a combination of traffic rules, social norms, courtesy, hand signals, and common sense. In this paper, we identify and classify such Dynamic Intersections that might lead to vehicle accidents and/or deadlocks and that might appear almost anytime and anywhere on public roads. In addition, we present a cooperative dynamic intersection protocol that uses on-board perception systems and vehicular communications for peer-to-peer negotiation. Under our protocol, autonomous driving vehicles can create a vehicular communication-based traffic manager named Cyber Traffic Light when congestion arises. Cyber Traffic Light works as a self-organizing, self-planning, and self-optimizing traffic manager, and it allocates the green period for vehicles coming from the multiple directions. Finally, we showed that our decentralized protocol has much higher traffic throughput, compared to two simple protocols while guaranteeing road safety.

Is the automated vehicle “aware” of the pedestrian? Examining driving behavior adaptation as a cue to inform the passenger of a potential hazard

In urban areas, drivers often have difficulty detecting pedestrians who want to cross the street but are obstructed by parked vehicles. Highly automated vehicles (HAVs, SAE Level 4) will have to solve this issue automatically and at least as safely as human drivers. We conducted two studies to investigate whether passengers’ trust in automation can be supported by adapting HAV driving behavior to demonstrate that the vehicle is “aware” of a potential hazard. We examined the passengers’ perception of risk, their trust in the automated driving system, and their preferences for HAV behavioral adaptations as a response to potential hazard in a driving situation in two experimental studies, 1) a driving simulator study and 2) a human&vehicle-in-the-loop study. We varied the lateral offset from the road center and deceleration (driving simulator only) when passing a parked vehicle. In the driving simulator study, we varied the HAV’s speed and the presence of a pedestrian in the parking bay and an oncoming vehicle. In the human&vehicle-in-the-loop study, the HAV passing the obscured pedestrian at 50 km/h. Results support the idea that it is beneficial for human passengers to be informed about the HAVs “awareness” of a potentially hazardous situation. However, introducing a lateral offset had no positive effect on passengers’ perceptions. Deceleration enhanced passengers’ trust but was only examined in the driving simulator study. Future research should compare the effects of behavioral adaptations to other in-vehicle human–machine interface approaches providing information about the “awareness” about potential hazards visually.

Effect of multiple monitoring requests on vigilance and readiness by measuring eye movement and takeover performance

Drivers do not need to supervise the L3 automated driving system but have to resume dynamic driving tasks when necessary, where the takeover request system plays a crucial role in alerting drivers to take over control. Previous studies have investigated the feasibility of the two-stage takeover system, which divides the takeover request system into monitoring requests and takeover requests. However, whether it will affect drivers’ situation awareness and takeover performance if the monitoring request is issued multiple times without the takeover request has not been fully considered. This research conducted a driving simulation study to investigate the effect of multiple monitoring requests on participants’ vigilance to monitoring requests and readiness for takeover requests by measuring their eye movement and takeover performance. Forty-two participants experienced three kinds of takeover conditions: no monitoring request, once monitoring request, and four monitoring requests. Simultaneously, the eye movement and takeover performance of participants were recorded and analyzed. Results showed that participants gained sufficient situation awareness in the monitoring request phase and showed better takeover performance in the two-stage takeover system than the single-stage takeover system. However, participants’ vigilance to the monitoring request and readiness for the takeover request decreased after experiencing multiple monitoring requests. Specifically, 62% and 59% of participants put their hands on the steering wheel during the monitoring request phase when experiencing the first monitoring request, which decreased to 35% after experiencing four monitoring requests. Moreover, after experiencing multiple monitoring requests, more participants shifted their attention to non-driving-related tasks after first observing the road and with a reduced percentage of time in the road center. These findings suggest that the monitoring request should be more reasonable to avoid reducing the vigilance and readiness of drivers to take advantage of the two-stage takeover system effectively.

Approaching intersections: Gaze behavior of drivers depending on traffic, intersection type, driving maneuver, and secondary task involvement

Urban intersections are hotspots for crashes because they provide a location for several traffic streams and types of road users to cross. A main cause of crashes is the misinformation of drivers as they fail to sense relevant visual information. We aimed to analyze the gaze behavior of car drivers in a variety of intersection scenarios, bringing together the partial findings of previous research, and examine the interdependencies of the contributing factors to provide a database for driver modeling. In a driving simulator study with 59 participants, we varied intersection scenarios regarding drivers’ right of way (yield sign, green traffic light), intersection type (T junction, X intersection), surrounding traffic (none, irrelevant, relevant), and intended driving maneuver (left turn, right turn, going straight). A total of 25 intersection scenarios were presented in a within-subjects design to a control group and a group with a cognitive load task (counting back in numbers of two). Fixations were coded regarding defined areas of interest in the field of view and separated according to three segments of the intersection approach: 75–50 m, 50–25 m, and 25–0 m before entering the intersection. The results show that the effect of surrounding traffic, secondary task engagement, and the intended driving maneuver changed dramatically depending on the right of way of the driver. Surrounding traffic primarily affected gaze behavior in scenarios of ceding the right of way close to the intersection entry. The cognitive load task increased fixations on the road center especially in situations where the driver had the right of way, but less in situations of ceding the right of way. Interactions with the type of intersection were only apparent for different driving maneuvers. This study provides a detailed and comprehensive picture of drivers’ attentional processes when approaching intersections which is relevant for understanding and modeling of driver behavior in urban traffic.