Analysis of Vehicle Trajectories for Determining Cross-Sectional Load Density Based on Computer Vision

Špaňhel, Jakub; Juránek, Roman; Herout, Adam; Novák, Jan; Havránek, Pavel

doi:10.1109/itsc.2019.8917374

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…This calibration enabled to map each image point onto a road plane and conduct measurements and calculations in real-world units (metres). For more details on the process, see Špaňhel et al [27].…”

Section: Camera Calibrationmentioning

confidence: 99%

How does road marking in horizontal curves influence driving behaviour?

Havránek

Zůvala

Špaňhel

et al. 2020

Eur. Transp. Res. Rev.

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Purpose Given the inconsistent application of various road markings on Czech rural roads, there is a question “How does road marking in horizontal curves influence driving behaviour?” The study objective was to assess how centreline and edgelines influence driving behaviour. Methods To focus on the critical conditions, six curves on secondary rural roads, with radii below 200 m, were selected and monitored before and after application of road marking. The studied indicators were average speed and lateral position, which were collected using trajectories detected in calibrated video recordings. Results The results indicated that speeds decreased in both edgeline and centreline applications; regarding lateral positions, the edgelines were associated with shifting the driving trajectories towards the centre of the road, and the centrelines were associated with shifting the driving trajectories towards the road edges. Conclusions The indicated trends are likely to be influenced also by other factors, such as specific curve radii values, superelevation, speed profile, or parameters of road surroundings. Following study should thus focus on collecting data in a larger sample of sites and building a cross-sectional model, statistically linking the mentioned characteristics with safety.

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Section: Camera Calibrationmentioning

confidence: 99%

How does road marking in horizontal curves influence driving behaviour?

Havránek

Zůvala

Špaňhel

et al. 2020

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…These data can be used to analyze traffic behavior such as speed, lane change, violation of the traffic rules [ 2 ], and traffic flow [ 3 ]. In addition, it can be used not only for traffic management and control [ 4 ] and real-time traffic situation state estimation [ 5 ], but also for accident and dangerous situation recognition and prediction [ 6 , 7 , 8 , 9 , 10 , 11 ]. Hence, the practicality of using vehicle trajectories has become invaluable.…”

Section: Introductionmentioning

confidence: 99%

3D Vehicle Trajectory Extraction Using DCNN in an Overlapping Multi-Camera Crossroad Scene

Heo

Kwon

2021

Sensors

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The 3D vehicle trajectory in complex traffic conditions such as crossroads and heavy traffic is practically very useful in autonomous driving. In order to accurately extract the 3D vehicle trajectory from a perspective camera in a crossroad where the vehicle has an angular range of 360 degrees, problems such as the narrow visual angle in single-camera scene, vehicle occlusion under conditions of low camera perspective, and lack of vehicle physical information must be solved. In this paper, we propose a method for estimating the 3D bounding boxes of vehicles and extracting trajectories using a deep convolutional neural network (DCNN) in an overlapping multi-camera crossroad scene. First, traffic data were collected using overlapping multi-cameras to obtain a wide range of trajectories around the crossroad. Then, 3D bounding boxes of vehicles were estimated and tracked in each single-camera scene through DCNN models (YOLOv4, multi-branch CNN) combined with camera calibration. Using the abovementioned information, the 3D vehicle trajectory could be extracted on the ground plane of the crossroad by calculating results obtained from the overlapping multi-camera with a homography matrix. Finally, in experiments, the errors of extracted trajectories were corrected through a simple linear interpolation and regression, and the accuracy of the proposed method was verified by calculating the difference with ground-truth data. Compared with other previously reported methods, our approach is shown to be more accurate and more practical.

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“…calculation and prediction [ 3 , 4 , 5 ] and so on. Based on these data, traffic state estimation [ 6 , 7 ] and traffic management and control [ 8 ] can be conducted, which plays a key role in ensuring traffic efficiency and is of great research significance and practical value.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Spatial Distribution and 3D Trajectory Extraction Algorithm in a Cross-Camera Traffic Scene

Tang

Song

Wang

et al. 2020

Sensors

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The three-dimensional trajectory data of vehicles have important practical meaning for traffic behavior analysis. To solve the problems of narrow visual angle in single-camera scenes and lack of continuous trajectories in 3D space by current cross-camera trajectory extraction methods, we propose an algorithm of vehicle spatial distribution and 3D trajectory extraction in this paper. First, a panoramic image of a road with spatial information is generated based on camera calibration, which is used to convert cross-camera perspectives into 3D physical space. Then, we choose YOLOv4 to obtain 2D bounding boxes of vehicles in cross-camera scenes. Based on the above information, 3D bounding boxes around vehicles are built with geometric constraints which are used to obtain projection centroids of vehicles. Finally, by calculating the spatial distribution of projection centroids in the panoramic image, 3D trajectories of vehicles are extracted. The experimental results indicate that our algorithm can effectively complete vehicle spatial distribution and 3D trajectory extraction in various traffic scenes, which outperforms other comparison algorithms.

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Analysis of Vehicle Trajectories for Determining Cross-Sectional Load Density Based on Computer Vision

Cited by 4 publications

References 9 publications

How does road marking in horizontal curves influence driving behaviour?

How does road marking in horizontal curves influence driving behaviour?

3D Vehicle Trajectory Extraction Using DCNN in an Overlapping Multi-Camera Crossroad Scene

Vehicle Spatial Distribution and 3D Trajectory Extraction Algorithm in a Cross-Camera Traffic Scene

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