Complex Traffic Situations Research Articles

Making Bertha Drive—An Autonomous Journey on a Historic Route

125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.

Research on Energy Consumption of Two-Lane Traffic Based on Turn Signal Effect

Based on the STCA model, an improved two-lane traffic CA model considering turn signals is proposed. The model designs driver behavior changes because of the effect of turn light of front vehicle,and introduces rules of changing lanes, cutting lanes and yielding the right of way for the rear vehicle. Through computer numerical simulations, this study has presented curve diagrams of relationship between turning rate and energy consumption and also analyzed the effects of driver behavior of the rear vehicles on traffic flow. The results indicate that increased probability of lane change make more turning rate and energy consumption. When probability of lane change is from 0.5 to 1.0, turning rate increases 13.23% and energy consumption increases 22.15% . Compared with the traditional model, STCA model can depict more complex traffic situations and reproduce driver behaviors and more realistic process of traffic.

Bottleneck Simulation of Two-Lane Traffic CA Model with Turn Signal

Based on the STCA model, an improved two-lane traffic CA model in view of turn signals is proposed. The model considers changes in driver behavior before and after the signaling of the leading vehicle, and introduces rules of changing lanes, cutting lanes and yielding the right of way for the flowing vehicle. Through computer numerical simulations, this study has presented time-space diagrams and also analyzed the effects of driver behavior of the rear vehicles on traffic flow. The results reveal that a higher rate of cutting lanes incurs a heavier traffic flow in normal situations while yielding the right of way relieves traffic congestion in a bottleneck situation. Compared with the traditional model, STCA model can depict more complex traffic situations resulted from different driver behaviors and reproduce a more realistic process of traffic flow.

Development of a methodology to demonstrate the environmental impact of connected vehicles under lane-changing conditions

This paper describes a lane-change model for connected vehicles, and evaluates the environmental impact per road level of service (LOS) when a host vehicle makes a lane change. During manual driving, the host vehicle accelerates or decelerates to create a safe distance before the vehicle makes a lane change. During automated driving, however, the host vehicle makes lane changes in response to the acceleration or deceleration of the vehicle in front of it through vehicle-to-vehicle communications. The gap, safe distance, and variation in vehicle speed depending on the LOS are analyzed using simulation for both automated and manual driving. For lane changing from a faster to a slower lane, the reduction in CO2 emissions of the connected vehicle was in the range 4770–54,291 g/km in comparison to the manual vehicle. For lane changing from a slower to a faster lane, the CO2 reductions were in the range 40,788–91,884 g/km. In more complex traffic situations, the CO2 reductions of the connected vehicles were larger than in simple traffic situations. This study indicates that the use of connected vehicles can result in a reduction of CO2 emissions.

Based on Rear-End Morphological Analysis of Collision Avoidance Algorithm

Rear-end collision is one kind of traffic accident with extreme danger. Peoples security and property are serious threatened by this kind of accidents. Based on morphological research of real-world collisions, this paper makes quantitative analysis of minimum safe distance under complex traffic situations. A novel mathematics model and corresponding collision avoidance algorithms are proposed. To verify feasibility of this algorithm, this paper performs accident emulation and data analysis with Visual Basic 6.0. Experimental results show that this approach is capable of reducing occurrence possibility of rear-end collisions.

Inexperience and risky decisions of young adolescents, as pedestrians and cyclists, in interactions with lorries, and the effects of competency versus awareness education

BackgroundRoad injuries are a prime cause of death in early adolescence. Often road safety education (RSE) is used to target risky road behaviour in this age group. These RSE programmes are frequently based on the assumption that deliberate risk taking rather than lack of competency underlies risk behaviour. This study tested the competency of 10–13 year olds, by examining their decisions – as pedestrians and cyclists – in dealing with blind spot areas around lorries. Also, the effects of an awareness programme and a competency programme on these decisions were evaluated. MethodTable-top models were used, representing seven scenarios that differed in complexity: one basic scenario to test the identification of blind spot areas, and 6 traffic scenarios to test behaviour in traffic situations of low or high task complexity. Using a quasi-experimental design (pre-test and post-test reference group design without randomization), the programme effects were assessed by requiring participants (n=62) to show, for each table-top traffic scenario, how they would act if they were in that traffic situation. ResultsOn the basic scenario, at pre-test 42% of the youngsters identified all blind spots correctly, but only 27% showed safe behaviour in simple scenarios and 5% in complex scenarios. The competency programme yielded improved performance on the basic scenario but not on the traffic scenarios, whereas the awareness programme did not result in any improvements. The correlation between improvements on the basic scenarios and the traffic scenarios was not significant. ConclusionsYoung adolescents have not yet mastered the necessary skills for safe performance in simple and complex traffic situations, thus underlining the need for effective prevention programmes. RSE may improve the understanding of blind spot areas but this does not ‘automatically’ transfer to performance in traffic situations. Implications for the design of RSE are discussed.

Findings from the Candrive/Ozcandrive study: Low mileage older drivers, crash risk and reduced fitness to drive

Previous research has found that only older drivers with low annual driving mileages had a heightened crash risk relative to other age groups. These drivers tend to drive mainly in urban areas, where the prevalence of complex traffic situations increases crash risk. However it might also be that some drivers may have reduced their driving due to perceived or actual declines in driving fitness.This paper uses Canadian and Australian data from the Candrive/Ozcandrive older driver study to investigate the association between annual driving distances and a set of driving-related factors, including fitness to drive.All drivers in the Candrive/Ozcandrive older driver cohort study were allocated to one of three groups according to their self-reported annual driving distances: <5001km; >5000 and <15,000km; and 15,000km or greater. Relationships between these driving-distance categories and: (a) self-reported crash data; (b) various Year 1 ‘fitness to drive’ performance measures; and (c) self-perceptions of driving ability and of comfort while driving, were determined.Results confirmed the previously reported association between low mileage and heightened crash risk. Further, low mileage drivers performed relatively poorly on a wide range of performance measures, perceived their own driving ability as lower, and reported lower comfort levels when driving in challenging situations, compared to the higher mileage drivers. In most instances, these differences were statistically significant.The paper provides further evidence that the so-called ‘older driver problem’ is most pertinent to low mileage drivers, and that this is due in part to low mileage drivers tending to have reduced fitness to drive. This higher risk group represented a fairly small proportion of the sample in this study.

A Traffic-Flow-Phase Adaptive Routing Algorithm for VANETs

Vehicular ad-hoc Networks (VANETs) has become a hot topic in recent years, where the routing protocol has also become research focus. The vast majority of the existing routing protocols are designed for a single scenario, thus it is difficult for them to be extended for the complicated traffic environment. In this paper we propose a new routing algorithm X-CAST for vehicular communication on highway, which can be adaptive to different phases of traffic flow and guarantee reliable transmission in different environments. The experimental results reveal that X-CAST performs much better than other traditional routing protocols in complex traffic flow situation.

스테레오 비전기반의 컬럼 검출과 조감도 맵핑을 이용한 전방 차량 검출 알고리즘

본 논문에서는 스테레오 비전기반의 컬럼 검출과 조감도 맵핑을 이용한 전방 차량 검출 알고리즘을 제안한다. 제안된 알고리즘은 실제 복잡한 도로 환경에서 전방 차량을 강건하게 검출할 수 있다. 전체적인 알고리즘은 도로 특징기반의 컬럼 검출, 조감도 기반의 장애물체 세그멘테이션, 차량 특징기반의 영역 재결합, 차량 검증으로 크게 네 단계로 구성되어 있다. 먼저 v-시차맵상에서 최대 빈도값을 이용하여 도로 특징 정보만을 추출한 후, 이를 기반으로 컬럼 검출을 수행한다. 도로 특징 정보는 기존의 중앙값과 달리 도로 환경에 영향을 받지 않아 도로상의 장애물체 유무를 판단하는 기준으로 적절하다. 그러나 다수의 장애물체가 동일한 장애물체로 검출되는 것을 해결하기 위하여 조감도 기반의 세그멘테이션을 수행한다. 조감도는 시차맵과 카메라 정보를 기반으로 계산된 장애물체들의 위치를 평면상에 표시함으로써 장애물체를 쉽게 분리할 수 있다. 그러나 분리된 장애물체 중에는 동일한 장애물체인 경우도 있으므로, 도로상의 차량 특징을 기반으로 장애물체가 동일한지를 판단하여 재결합하는 과정을 수행한다. 마지막으로 시차맵과 그레이 영상기반의 차량 검증 단계를 수행하여 차량만 검출한다. 제안된 알고리즘을 실제 복잡한 도로 영상에 적용함으로써 차량 검증 성능을 검증한다. In this paper, we propose a forward vehicle detection algorithm using column detection and bird's-eye view mapping based on stereo vision. The algorithm can detect forward vehicles robustly in real complex traffic situations. The algorithm consists of the three steps, namely road feature-based column detection, bird's-eye view mapping-based obstacle segmentation, obstacle area remerging and vehicle verification. First, we extract a road feature using maximum frequent values in v-disparity map. And we perform a column detection using the road feature as a new criterion. The road feature is more appropriate criterion than the median value because it is not affected by a road traffic situation, for example the changing of obstacle size or the number of obstacles. But there are still multiple obstacles in the obstacle areas. Thus, we perform a bird's-eye view mapping-based obstacle segmentation to divide obstacle accurately. We can segment obstacle easily because a bird's-eye view mapping can represent the position of obstacle on planar plane using depth map and camera information. Additionally, we perform obstacle area remerging processing because a segmented obstacle area may be same obstacle. Finally, we verify the obstacles whether those are vehicles or not using a depth map and gray image. We conduct experiments to prove the vehicle detection performance by applying our algorithm to real complex traffic situations.

Older Drivers' “High Per-Mile Crash Involvement”: The Implications for Licensing Authorities

Objective. Determine what role motor vehicle administrators have in dealing with older drivers based on their crash risk and mobility needs. Method. This article reviews both the recent crash, injury, and exposure trends from the National Household Travel Survey and other sources to help motor vehicle administrators set priorities in meeting the safety and mobility needs of older persons. Results. Older drivers have a higher crash risk per mile driven. The “risk” of dying in a crash is more likely attributable to the frailty of older drivers than the risks associated with the functional limitations that accompany aging. The research literature indicates that older drivers are not a risk to other road user age groups but primarily to themselves. Furthermore, recent fatality data indicate that the death rates for older persons, particularly those over 80 years of age, have been declining over the last 10 years. These results differ from recent fatality trend projections that predicted a significant increase in traffic fatalities associated with an aging driver population. Drivers over 80 are more likely to have a crash on a per licensed driver basis. There is new evidence, however, that only older drivers who drive infrequently are at increased risk. These drivers drive relatively short distances on local streets with complex traffic situations. Most of these drivers are also likely to be experiencing multiple functional limitations. Since those who stop driving reduce their mobility by over one half, motor vehicle administrators need to work more closely with the state departments of aging and transportation providers to help ensure mobility options for those who can no longer drive. Conclusions. Older driver motor vehicle crashes are not a significant threat to other road users in vehicles or as pedestrians. It is the older drivers and their vehicle occupants who are at higher risk of dying when in a crash. Current evidence is that the drivers who pose the greatest risks are the teenage grandchildren of this older generation. Therefore, motor vehicle administrators can support their stand against increasing the licensing requirements of older drivers. When dealing with the older driver population they need to weigh the consequences of having someone stop driving and assist other responsible agencies in fostering sustained mobility for those who stop driving.

Delays, inaccuracies and anticipation in microscopic traffic models

We generalize a wide class of time-continuous microscopic traffic models to include essential aspects of driver behaviour not captured by these models. Specifically, we consider (i) finite reaction times, (ii) estimation errors, (iii) looking several vehicles ahead (spatial anticipation), and (iv) temporal anticipation. The estimation errors are modelled as stochastic Wiener processes and lead to time-correlated fluctuations of the acceleration. We show that the destabilizing effects of reaction times and estimation errors can essentially be compensated for by spatial and temporal anticipation, that is, the combination of stabilizing and destabilizing effects results in the same qualitative macroscopic dynamics as that of the, respectively, underlying simple car-following model. In many cases, this justifies the use of simplified, physics-oriented models with a few parameters only. Although the qualitative dynamics is unchanged, multi-anticipation increase both spatial and temporal scales of stop-and-go waves and other complex patterns of congested traffic in agreement with real traffic data. Remarkably, the anticipation allows accident-free smooth driving in complex traffic situations even if reaction times exceed typical time headways.

Investigation on Road Traffic Safety of the Mountain Areas in Southwest China Based on Wulong County, Chongqing Municipality

Objective: To study the present situation, characteristics, and main problems of the traffic safety in the mountain areas of Southwest China. Methods: A study was done on the traffic status of Wulong County, Chongqing Municipality, by means of document review, field investigation, and inquiry. Results: The traffic crashes and deaths in Wulong County were increasing since 1999. From 1997 to 2001, there occurred 126 severe and very severe traffic crashes resulting in 175 deaths and 411 serious injuries, which accounted for 9.4% of total crashes, 96.1% of total deaths, and 48.4% of total serious injuries, respectively. The severe and very severe traffic crashes were highest in 2000. One part for 83 km in length accounting for 3.0% of No. 319 National Highway was responsible for 54.5% of very severe traffic crashes, which closely related to conditions of the road itself and its environment and defective execution of traffic laws. Conclusions: Wulong County, one of the mountain counties in Southwest China, is now facing a complex and serious road traffic situation and serious traffic safety, with a high incidence of severe and very severe road traffic crashes. The crashes closely relate to imperfect road design, defective execution of traffic laws, imperfect management, and environmental factors.

Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems

One key technology of intelligent transportation systems is the use of advanced sensor systems for on-line surveillance to gather detailed information on traffic conditions. Traffic video analysis can provide a wide range of useful information to traffic planners. In this context, the object-level indexing of video data can enable vehicle classification, traffic flow analysis, incident detection and analysis at intersections, vehicle tracking for traffic operations, and update of design warrants. In this paper, a learning-based automatic framework is proposed to support the multimedia data indexing and querying of spatio-temporal relationships of vehicle objects in a traffic video sequence. The spatio-temporal relationships of vehicle objects are captured via the proposed unsupervised image/video segmentation method and object tracking algorithm, and modeled using a multimedia augmented transition network model and multimedia input strings. An efficient and effective background learning and subtraction technique is employed to eliminate the complex background details in the traffic video frames. It substantially enhances the efficiency of the segmentation process and the accuracy of the segmentation results to enable more accurate video indexing and annotation. The paper uses four real-life traffic video sequences from several road intersections under different weather conditions in the study experiments. The results show that the proposed framework is effective in automating data collection and access for complex traffic situations.

Improving Street Name Sign Legibility for Older Drivers

Reading street name signs is an important part of the navigation task of driving a vehicle. Older drivers, in particular, often have trouble reading street name signs because of poor sign design and because of vision problems associated with aging. Past research has shown that older drivers can benefit in sign detection by either increasing the size of the sign or by increasing the retro-reflectivity of the material used to make the sign. The present study examined the effects of reflective material on the legibility distance for a standard size shoulder mounted street name sign. ASTM Types I, III, VII, and IX were used for the white on green signs with legend consisting of a 6“ initial upper case letter and 4.5” lower case Highway Series C. The effects of intersection complexity were examined by conducting the study at three different intersections in an urban area that varied in traffic volumes, commercial lighting and geometric design. An additional variable was which side of the street the signs appeared. Older drivers, with a mean age of 71, drove an instrumented vehicle in real traffic at night and were asked to read traffic signs temporarily erected for purposes of the study. The results showed an interaction between sign reflectivity and intersection complexity, suggesting that brighter signs aid legibility more in complex traffic situations. The differences between the reflective material types at low-complexity intersection were small compared to the differences observed at the high-complexity intersection where ASTM Types VII and IX showed greater legibility distances than Types I and III. Likewise, when signs were mounted on the left side of the street, the reflectivity of the material showed more of an effect than when they were mounted on the right side of the street. Overall, sign legibility distances were improved 21–30 % by changing from ASTM Types I and III to the higher reflectivity materials, Types VII and IX.

PERSONALITY AND INDIVIDUAL DIFFERENCES

Previous research has found that only older drivers with low annual driving mileages had a heightened crash risk relative to other age groups. These drivers tend to drive mainly in urban areas, where the prevalence of complex traffic situations increases crash risk. However it might also be that some drivers may have reduced their driving due to perceived or actual declines in driving fitness.This paper uses Canadian and Australian data from the Candrive/Ozcandrive older driver study to investigate the association between annual driving distances and a set of driving-related factors, including fitness to drive.All drivers in the Candrive/Ozcandrive older driver cohort study were allocated to one of three groups according to their self-reported annual driving distances: <5001 km; >5000 and <15,000 km; and 15,000 km or greater. Relationships between these driving-distance categories and: (a) self-reported crash data; (b) various Year 1 ‘fitness to drive’ performance measures; and (c) self-perceptions of driving ability and of comfort while driving, were determined.Results confirmed the previously reported association between low mileage and heightened crash risk. Further, low mileage drivers performed relatively poorly on a wide range of performance measures, perceived their own driving ability as lower, and reported lower comfort levels when driving in challenging situations, compared to the higher mileage drivers. In most instances, these differences were statistically significant.The paper provides further evidence that the so-called ‘older driver problem’ is most pertinent to low mileage drivers, and that this is due in part to low mileage drivers tending to have reduced fitness to drive. This higher risk group represented a fairly small proportion of the sample in this study.

Age-related Differences in Driving: Are Older Drivers More Serial?

A measurement-instrumented car was used to test the hypothesis that older drivers organise their car-controlling movements in a more serial way in order to reduce the momentary mental workload produced by complex traffic situations. Forty-two older drivers (65+, mean age 67.5) and 36 young middle-aged drivers (26-49, mean age 36.8) drove a measurement-instrumented car around a 7.4 km route in normal traffic. The use of car controls (e.g. clutch, brake) was registered by sensors connected to a computer. In the most complex parts of the test route (e.g. during which three or more controls were in use simultaneously), the older drivers tended to use three controls rather than four or more, whereas the younger group had a greater percentage of four or more controls. This percentage was positively related to speed. However, although older drivers drove more slowly in general, in pairs matched for speed, they still drove in a less parallel manner. Irrespective of age, parallel use of controls was positively related to the level of skill as indicated by the amount of cumulated driving experience. The shift towards more serial operation of controls possibly represents an empirical demonstration of a compensatory mechanism allowing older drivers to maintain their level of performance.

Identifying Gaps in Child Pedestrian Safety: Comparing What Children Do with What Parents Teach

Vehicle/pedestrian conflicts are a primary cause of deaths among children age 5 to 12 in North America. Children under 14 are twice as likely as the general population to be involved in vehicle/pedestrian accidents. Parents may overestimate the cognitive abilities of children to handle complex traffic situations and may assume that pedestrian rules taught to children at a young age will help safeguard them when crossing independently. For this reason, parental guidance on crossing safety was compared to actual practice by children, with and without accompanying parents or baby-sitters. Investigations were conducted in Kitchener-Waterloo, Ontario, a midsize, urban community. Interviews were done of a small sample (30) of well-educated parents in the vicinity of facilities used by children. Their responses were compared with observations of children (208) crossing at signalized and nonsignalized intersections in the same areas. While a high proportion of parents, 87 percent, teach safety practices, and 70 percent restate them when crossing with their children, 20 percent do not make unobserved checks of their children. Of those parents who do make unobserved checks, 42 percent found that none of their safety rules were being followed. Observations of children, not necessarily related to the adults interviewed, found that children were less likely to search for traffic at signalized than at nonsignalized intersections ( p &lt; 0.05). At nonsignalized intersections, 33 percent of unaccompanied children performed no visual search before crossing, increasing to 48 percent at signalized intersections. Even smaller numbers (&lt; 6 percent) of unaccompanied children looked behind them for turning vehicles. These findings suggest that, while more research is needed to improve parental training and child response, more emphasis should be placed on responsibilities of drivers and on countermeasures in the traffic environment.

Computer-assisted collision avoidance using ARPA and ECDIS

In the project entitled “Computer-Assisted Collision Avoidance”, which was funded by the German Mi nistry of Education, Research and Technology and was executed in cooperation with the industrial partner STN ATLAS Elektronik, the intention was to combine the tasks of track planning, track control and collision avoidance. The aim was to develop a system which would support the officer of the watch in the handling of complex traffic situations by suggesting collision-avoidance tracks. The main points of this project were in the following areas: p]Determination and display of danger areas The positions of possible collisions with other ships, and areas to be avoided on the basis of defined closest points of approach, are computed and displayed with geometrical exactness. The target ships are fed into the system via a standardized interface.

Deciding the Behaviour of an Autonomous Mobile Road Vehicle

The seeing passenger car VaMoRs-P is able to perform basic manoeuvres, e.g. lane keeping, convoy driving, and lane changing. In order to make it capable of performing an appropriate behaviour even in complex traffic situations, an algorithm was designed which assesses the traffic situation, determines a safety measure of different possible manoeuvres, and chooses one manoeuvre using a rule-based system. The transputer based implementation of this algorithm was demonstrated in real world traffic situations on French motorways.

Road Recognition in Complex Traffic Situations

This paper describes the algorithms for road edge detection in complex traffic situations by an on-board video camera. The information that comes from the motion of a dynamic scene is used to build a new family of filters called Dynamic Time Filters (DTF). These filters apply morphological opening and closing transformations to the image sequence provided by the camera. This sequence is considered as a 3D image. The DTF-filtered image is segmented by means of the watershed transformation. Two different modes have been implemented for the segmentation. The acquisition mode allows an initial extraction of the current traffic lane. It also produces initial markers which will be used by the second segmentation mode, where an interpolated road/lane model is used to follow and segment the lanes.Some results of the segmentation process are given in complex situations to illustrate the efficiency and robustness of this approach. Information is also given on the real-time performance of the algorithms implemented on a car demonstrator.