Search Results (18)

Given the severe global traffic safety challenges—including threats to human lives and socioeconomic impacts—this study analyzes visual behavior to promote sustainable transportation, improve road safety, and reduce resource waste and pollution caused by accidents. Four typical road sections, namely, turning, straight ahead, uphill, and downhill, were selected, and the eye movement data of 23 drivers in different driving stages were collected by aSee Glasses eye-tracking device to analyze the visual gaze characteristics of the drivers and their transfer patterns in each road section. Using Markov chain theory, the probability of staying at each gaze point and the transfer probability distribution between gaze points were investigated. The results of the study showed that drivers’ visual behaviors in different road sections showed significant differences: drivers in the turning section had the largest percentage of fixation on the near front, with a fixation duration and frequency of 29.99% and 28.80%, respectively; the straight ahead section, on the other hand, mainly focused on the right side of the road, with 31.57% of fixation duration and 19.45% of frequency of fixation; on the uphill section, drivers’ fixation duration on the left and right roads was more balanced, with 24.36% of fixation duration on the left side of the road and 25.51% on the right side of the road; drivers on the downhill section looked more frequently at the distance ahead, with a total fixation frequency of 23.20%, while paying higher attention to the right side of the road environment, with a fixation duration of 27.09%. In terms of visual fixation, the fixation shift in the turning road section was mainly concentrated between the near and distant parts of the road ahead and frequently turned to the left and right sides; the straight road section mainly showed a shift between the distant parts of the road ahead and the dashboard; the uphill road section was concentrated on the shift between the near parts of the road ahead and the two sides of the road, while the downhill road section mainly occurred between the distant parts of the road ahead and the rearview mirror. Although drivers’ fixations on the front of the road were most concentrated under the four road sections, with an overall fixation stability probability exceeding 67%, there were significant differences in fixation smoothness between different road sections. Through this study, this paper not only reveals the laws of drivers’ visual behavior under different driving environments but also provides theoretical support for behavior-based traffic safety improvement strategies. Full article

Accidents involving cyclists and trucks are among the most severe road accidents. In 2021, 199 cyclists were killed in accidents involving a truck in the EU. The main accident situation is a truck turning right and a cyclist going straight ahead. A large proportion of these accidents are caused by the inadequate visibility in an HGV (Heavy Goods Vehicle). The blind spot, in particular, is a significant contributor to these accidents. A BSD (Blind Spot Detection) system is expected to significantly reduce these accidents. There are only a few studies that estimate the potential of assistance systems, and these studies include a combined assessment of cyclists and pedestrians. In the present study, accident simulations are used to assess a warning and an autonomously intervening assistance system that could prevent truck to cyclist accidents. The main challenges are local sight obstructions such as fences, hedges, etc., rule violations by cyclists, and the complexity of correctly predicting the cyclist’s intentions, i.e., detecting the trajectory. Taking these accident circumstances into consideration, a BSD system could prevent between 26.3% and 65.8% of accidents involving HGVs and cyclists. Full article

In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals. Full article

The incidence of right-turning pedestrian accidents is increasing in South Korea. Most of the accidents occur when a large vehicle is turning right, and the main cause of the accidents was found to be the driver’s limited field of vision. After these accidents, the government implemented a series of institutional measures with the objective of preventing such accidents. However, despite the institutional arrangements in place, pedestrian accidents continue to occur. We focused on the many limitations that autonomous vehicles, like humans, can face in such situations. To address this issue, we propose a right-turn pedestrian collision avoidance system by installing a LiDAR sensor in the center of the intersection to facilitate pedestrian detection. Furthermore, the urban road environment is considered, as this provides the optimal conditions for the model to perform at its best. During this research, we collected data on right-turn accidents using the CARLA simulator and ROS interface and demonstrated the effectiveness of our approach in preventing such incidents. Our results suggest that the implementation of this method can effectively reduce the incidence of right-turn accidents in autonomous vehicles. Full article

Advancements in computer vision are rapidly revolutionizing the way traffic agencies gather roadway geometry data, leading to significant savings in both time and money. Utilizing aerial and satellite imagery for data collection proves to be more cost-effective, more accurate, and safer compared to traditional field observations, considering factors such as equipment cost, crew safety, and data collection efficiency. Consequently, there is a pressing need to develop more efficient methodologies for promptly, safely, and economically acquiring roadway geometry data. While image processing has previously been regarded as a time-consuming and error-prone approach for capturing these data, recent developments in computing power and image recognition techniques have opened up new avenues for accurately detecting and mapping various roadway features from a wide range of imagery data sources. This research introduces a novel approach combining image processing with a YOLO-based methodology to detect turning lane pavement markings from high-resolution aerial images, specifically focusing on Florida’s public roadways. Upon comparison with ground truth data from Leon County, Florida, the developed model achieved an average accuracy of 87% at a 25% confidence threshold for detected features. Implementation of the model in Leon County identified approximately 3026 left turn, 1210 right turn, and 200 center lane features automatically. This methodology holds paramount significance for transportation agencies in facilitating tasks such as identifying deteriorated markings, comparing turning lane positions with other roadway features like crosswalks, and analyzing intersection-related accidents. The extracted roadway geometry data can also be seamlessly integrated with crash and traffic data, providing crucial insights for policymakers and road users. Full article

This paper describes the development of an Advanced Driver Assistance System (ADAS) which will allow drivers to avoid collisions with an oncoming vehicle from an occluded area when turning right at intersections in left-hand traffic. Connected vehicles, in coordination with infrastructure, represent one of the commercialized ADAS technologies for collision avoidance. However, the coverage of the ADAS will be limited to designated intersections only, as communication equipment needs to be installed in both the vehicle and infrastructure to enable the assistance. This paper proposes an ADAS using on-board sensors, independent of infrastructure facilities, to control the vehicle velocity to avoid collisions. Most current intersection assistance, by using an Autonomous Emergency Braking System (AEBS), allows the driver to avoid a collision with oncoming vehicles when there is clear vision without occlusion. However, many accidents occur when the vehicle detects the oncoming vehicle too late because of occlusion in the intersection, such as a vehicle in the opposite lane. This system calculates the hazardous speed criteria of the ego vehicle, which might result in a high risk of collision when darting out occurs, and provides speed control assistance to allow the driver to escape from the hazardous speed region. The simulation results reveal that the proposed system effectively reduces the possibility of collisions compared to conventional AEBS. Full article

Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRUs) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system’s performance. The lateral distance between the right side of the commercial vehicle and the VRU, as well as obstacles between them, along with the speed difference can have great influence on the reliability of the assistance system. Depending on the concrete time of the system’s warning signal, the accident can be avoided or not by the driver when reacting to this signal. Full article

A thorough safety assessment of an automated driving system (ADS) is necessary before its introduction into the market and practical application. Scenario-based assessments have received significant attention in research. However, identifying sufficient critical scenarios for ADSs is a major challenge, especially for complex urban environments with a variety of road geometries, traffic rules, and traffic participants. To identify the critical scenarios in this complex environment, it is essential to understand the environmental factors that lead to safety-critical events (e.g., accidents and near-miss incidents). Thus, this study proposes a method for identification of critical scenario components by analyzing near-miss incident data and extracting environmental factors that induce driver errors. In this study, we applied the proposed method to a scenario, in which an ego vehicle makes a right turn at a signalized intersection with an oncoming vehicle approaching the intersection in left-hand traffic, as a case study. The proposed method identified two components (dynamic occlusion caused by oncoming right-turn vehicles and change in traffic lights) that were both critical and challenging for ADSs. The case study results showed the usefulness of the identified components and the validity of the proposed method, which can extract critical scenario components explicitly. Full article

Road intersections are shared among several conflicted traffic flows. Stop signs are used to control competing traffic flows at road intersections safely. Then, driving rules are constructed to control the competing traffic flows at these stop sign road intersections. Vehicles must apply a complete stop with no motion in front of stop signs. First to arrive, first to go, straight before turns, and right then left are the main driving rules at stop sign intersections. Drivers must be aware of the stop sign’s existence, the architecture of the road intersection, and traffic distribution in the competing traffic flows. This is to make the best decision to pass the intersection or wait for other conflicted flows to pass according to the current situation. Due to bad weather conditions, obstacles, or existing heavy vehicles, drivers may miss capturing the stop sign. Moreover, the architecture of the road intersection and the characteristics of the competing traffic flows are not always clear to the drivers. In this work, we aim to keep the driver aware ahead of time of the existing stop signs, the architecture of the road intersection, and the traffic characteristics of the competing traffic flow at the targeted destination. Moreover, the best speed and driving behaviors are recommended to each driver. This is based on his/her position and the distribution of the existing traffic there. A driving assistance protocol is presented in this paper based on vehicular network technology. Real-time traffic characteristics are gathered and analyzed of vehicles around the intersections. Then, the best action for each vehicle is recommended accordingly. The experimental results show that the proposed driving assistant protocol successfully enhances the safety conditions around road intersections controlled by stop signs. This is by reducing the percentage of accident occurrences. Fortunately, the traffic efficiency of these road intersections is also enhanced; the accident percentage is decreased by 25% upon using the proposed protocol. Full article

Pedestrian deaths account for the highest percentage of fatality caused by traffic accidents in Japan. Increasing pedestrian safety is a key objective for reducing such deaths. For pedestrian fatality caused by vehicles at low speed, turning the vehicle toward the right was the most common behavior under left-hand traffic. Autonomous emergency braking (AEB) systems for pedestrian safety have great potential to mitigate pedestrian injuries and fatalities in traffic accidents. However, pedestrian-AEB systems, especially for vehicles turning right, are still under development. This study identified the characteristics of dangerous traffic scenarios between vehicles turning right and pedestrians, focusing on two directions of pedestrian crossing: from the left to the right side (Left-Pedestrian) and from the right to the left side (Right-Pedestrian). The ego vehicle recorded near-miss incidents using a drive recorder. The results revealed that the Left-Pedestrian and Right-Pedestrian scenarios had different features for both the width of roads going to and through the intersection and the average of the travel speeds of the ego vehicles. They had similar characteristics in terms of the presence of other vehicle categories, but differences in the relationship of numbers and/or travel directions of other vehicles. The findings of this study will contribute to the development and evaluation of safety systems for preventing collisions between right-turning vehicles and pedestrians at intersections. Full article

The rollover tendency of upland farm machinery needs to be carefully considered because upland crop fields are typically irregular, and accidents frequently result in injuries and even death to the operators. In this study, the rollover characteristics of an underdeveloped 12 kW automatic onion transplanter were determined theoretically and evaluated through simulation and validation tests considering the mounting position of the transplanting unit and load conditions. The center of gravity (CG) coordinates for different mass distributions, and static and dynamic rollover angles were calculated theoretically. Simulation and validation tests were conducted to assess the static rollover angle under different mounting positions of the transplanting unit and load conditions of the onion transplanter. The dynamic rollover tendency was evaluated by operating the onion transplanter on different surfaces and at different speeds. According to the physical properties and mass of the onion transplanter, the theoretical rollover angle was 34.5°, and the coordinates of the CG gradually moved back to the rear wheel axle after attaching the transplanting part and under upward riding conditions. The average simulated rollover angle was 43.9°. A turning difference of 4.5° was observed between the right and left sides, where a 3° angle difference occurred due to the load variation. During the dynamic stability test, angle variations of 2~4° and 3~6° were recorded for both high and low driving speeds in the vehicle platform and transplanting unit, respectively. The overturning angles also satisfied the ISO standard. This study provides helpful information for ensuring the safety of upland crop machinery operating under rough and sloped field conditions. Full article

Several approaches from different perspectives have been used to solve problems with traffic accidents (TA), which mainly affect low- and middle-income countries. Conditions of certain cities, regarding road infrastructure, enforcement of traffic safety regulations, and motor vehicle numbers, influence the increase in TAs. Therefore, medium-sized cities in developing countries (context of interest), which commonly have worrying conditions, are a relevant scenario. One of the approaches to reduce TAs has been the use of data analysis through Machine Learning (ML); however, these techniques require a large amount of data, and medium-sized cities commonly do not have enough. Techniques such as Naturalistic Driving (ND) can be applied as a data collection method. This work proposes an intelligent collision risk detection system (ICDRS) using ND and ML to improve sustainability and safety of transportation in medium-sized cities. The ICRDS design considered the limitations of the context of interest and uses two data collection devices in the vehicle. The ICRDS validation included the design and execution of tests using ND. This validation identified if the collected data in a certain time interval contained high-risk collision events (sudden acceleration, sudden braking, aggressive left or right turn, aggressive left or right lane change). The system implementation results were satisfactory. The developed ML algorithm obtained an average value 0.98 in all the metrics. Two data sets of driving on routes were collected. In addition, the performed tests were able to identify city areas with high accident rates. Full article

The blind zone that accompanies the right-turn process of semitrailer trucks is a major cause of crashes and the high fatality of vulnerable road users (VRUs). Understanding the relationship between the blind zone and right-turn collisions will play a positive role in preventing such accidents. The purpose of this study was to investigate the formation of right-turn blind zones for semitrailer trucks and to determine the factors (turning speed, turning radius, and collision position) influencing the severity of accidents through real-world vehicle tests and PC-CRASH simulation. The results show that the calculation model of the inner wheel difference blind zone established for semitrailer trucks can provide more accurate estimation than the model for rigid trucks, due to the consideration of a virtual third axle between the tractor and the trailer. On the other hand, the PC-CRASH simulation test indicates the turning speed and turning radius directly affect the scale of the inner wheel difference blind zone, and larger blind zone and encroachment on adjacent lanes increase the potential for collision. Moreover, the difference in collision position is closely related to whether the rider suffers a secondary crush. Front position is more likely to cause the cyclist to be crushed. For further analysis, the long-term interaction between the blind zones resulting from the right rearview mirror and the inner wheel difference also increases the risk during a right turn. Therefore, reducing the blind zone in the right-turn process is the key to improving right-turn safety for semitrailer trucks and VRUs. Full article

Heavy goods vehicles (HGVs) are involved in 4.5% of police-reported road crashes in Europe and 14.2% of fatal road crashes. Active and passive safety systems can help to prevent crashes or mitigate the consequences but need detailed scenarios based on analysis of region-specific data to be designed effectively; however, a sufficiently detailed overview focusing on long-haul trucks is not available for Europe. The aim of this paper is to give a comprehensive and up-to-date analysis of crashes in the European Union that involve HGVs weighing 16 tons or more (16 t+). The identification of the most critical scenarios and their characteristics is based on a three-level analysis, as follows. Crash statistics based on data from the Community Database on Accidents on the Roads in Europe (CARE) provide a general overview of crashes involving HGVs. These results are complemented by a more detailed characterization of crashes involving 16 t+ trucks based on national road crash data from Italy, Spain, and Sweden. This analysis is further refined by a detailed study of crashes involving 16 t+ trucks in the German In-Depth Accident Study (GIDAS), including a crash causation analysis. The results show that most European HGV crashes occur in clear weather, during daylight, on dry roads, outside city limits, and on nonhighway roads. Three main scenarios for 16 t+ trucks are characterized in-depth: rear-end crashes in which the truck is the striking partner, conflicts during right turn maneuvers of the truck with a cyclist riding alongside, and pedestrians crossing the road in front of the truck. Among truck-related crash causes, information admission failures (e.g., distraction) were the main crash causation factor in 72% of cases in the rear-end striking scenario while information access problems (e.g., blind spots) were present for 72% of cases in the cyclist scenario and 75% of cases in the pedestrian scenario. The three levels of data analysis used in this paper give a deeper understanding of European HGV crashes, in terms of the most common crash characteristics on EU level and very detailed descriptions of both kinematic parameters and crash causation factors for the above scenarios. The results thereby provide both a global overview and sufficient depth of analysis of the most relevant cases and aid safety system development. Full article

Advanced driver assistance systems (ADAS) for crash avoidance, when making a right-turn in left-hand traffic or left-turn in right-hand traffic, are expected to further reduce the number of traffic accidents caused by automobiles. Accurate future trajectory prediction of an ego vehicle for risk prediction is important to activate the assistance system correctly. Our objectives are to propose a trajectory prediction method for ADAS for safe intersection turnings and to evaluate the effectiveness of the proposed prediction method. Our proposed curve generation method is capable of generating a smooth curve without discontinuities in the curvature. By incorporating the curve generation method into the vehicle trajectory prediction, the proposed method could simulate the actual driving path of human drivers at a low computational cost. The curve would be required to define positions, angles, and curvatures at its initial and terminal points. Driving experiments conducted at real city traffic intersections proved that the proposed method could predict the trajectory with a high degree of accuracy for various shapes and sizes of the intersections. This paper also describes a method to determine the terminal conditions of the curve generation method from intersection features. We set a hypothesis where the conditions can be defined individually from intersection geometry. From the hypothesis, a formula to determine the parameter was derived empirically from the driving experiments. Public road driving experiments indicated that the parameters for the trajectory prediction could be appropriately estimated by the obtained empirical formula. Full article