Collaborative approach for a safe driving distance using stereoscopic image processing

Olaverri-Monreal, Cristina; Krizek, Gerd Christian; Michaeler, Florian; Lorenz, R; Pichler, Matthias

doi:10.1016/j.future.2018.01.050

Cited by 18 publications

(13 citation statements)

References 3 publications

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“…In the same line of research but focusing on eliciting a behavior change from the driver in the following/rear vehicle, in [6,8], the stereoscopic capturing and processing of images by rear cameras was used to calculate in real time the distance between a leading and a following vehicle.…”

Section: Related Workmentioning

confidence: 99%

“…As an extension of the work presented in [8], in which a detailed error analysis of the distance calculation proposed in our solution was provided based on several measurement procedures and roadway geometry, we implement in this work a new software-architecture approach. We rely on the use of multithreading via 3rd party libraries as QT to investigate if the time difference between the stereoscopic cameras of 0.2 s that was due to the limitations of the previous implementation approach can be improved by integrating real-time capabilities in the system.…”

Section: Introductionmentioning

confidence: 99%

“…Our contribution consists of a novel visualization of messages that is independent of the communication capabilities of the following vehicle, being thus applicable for example in scenarios with low penetration rates of connected and autonomous vehicles [8].…”

Section: Introductionmentioning

confidence: 99%

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Empirical Analysis of Safe Distance Calculation by the Stereoscopic Capturing and Processing of Images Through the Tailigator System

Krizek

Hausleitner

Böhme

et al. 2019

Sensors

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Driver disregard for the minimum safety distance increases the probability of rear-end collisions. In order to contribute to active safety on the road, we propose in this work a low-cost Forward Collision Warning system that captures and processes images. Using cameras located in the rear section of a leading vehicle, this system serves the purpose of discouraging tailgating behavior from the vehicle driving behind. We perform in this paper the pertinent field tests to assess system performance, focusing on the calculated distance from the processing of images and the error margins in a straight line, as well as in a curve. Based on the evaluation results, the current version of the Tailigator can be used at speeds up to 50 km per hour without any restrictions. The measurements showed similar characteristics both on the straight line and in the curve. At close distances, between 3 and 5 m, the values deviated from the real value. At average distances, around 10 to 15 m, the Tailigator achieved the best results. From distances higher than 20 m, the deviations increased steadily with the distance. We contribute to the state of the art with an innovative low-cost system to identify tailgating behavior and raise awareness, which works independently of the rear vehicle’s communication capabilities or equipment.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Empirical Analysis of Safe Distance Calculation by the Stereoscopic Capturing and Processing of Images Through the Tailigator System

Krizek

Hausleitner

Böhme

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, performing traffic analysis using cameras has many advantages than other monitoring methods. First of all, the cameras are easy to install; additionally, they do not damage the roads and also have relatively low installation and maintenance costs [34,35]. Furthermore, a large number of cameras are already installed in the roads for surveillance purposes.…”

Section: Introductionmentioning

confidence: 99%

ATRIP: Architecture for Traffic Classification Based on Image Processing

et al. 2020

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With the increase of vehicles in large urban centers, there is also an increase in the number of traffic jams and accidents on public roads. The development of a proper Intelligent Transport System (ITS) could help to alleviate these problems by assisting the drivers on route selections to avoid the most congested road sections. Therefore, to improve on this issue, this work proposes an architecture to aid an ITS to detect, analyze, and classify the traffic flow conditions in real time. This architecture also provides a control room dashboard to visualize the information and notify the users about the live traffic conditions. To this end, the proposed solution takes advantage of computer vision concepts to extract the maximum information about the roads to better assess and keep the drivers posted about the traffic conditions on selected highways. The main contribution of the proposed architecture is to perform the detection and classification of the flow of vehicles regardless of the luminosity conditions. In order to evaluate the efficiency of the proposed solution, a testbed was designed. The obtained results show that the accuracy of the traffic classification rate is up to 90% in daylight environments and up to 70% in low light environments when compared with the related literature.

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“…After simplifying vehicle coordinates, the corresponding actual distance was obtained by calculating their Euclidean distance. C. Olaverri-Monreal et al [15] stereoscopically captured and processed images through the rear camera to obtain positional relationships between vehicles, with the distance between the preceding and rear vehicles calculated.…”

Section: Introductionmentioning

confidence: 99%

Research on Safety Lane Change Warning Method Based on Potential Angle Collision Point

Ding

Zheng

et al. 2019

Journal of Advanced Transportation

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In order to ensure safe lane change and avoid traffic accidents, an effective lane change assist system is required. In a lane change assist system, it is very important to obtain the following elements in time, such as actual distance between vehicles, minimum safety distance, and warning signal. To this end, this paper analyzed four kinds of lane change angle collision scenes. Initial position, initial velocity, acceleration, heading angle, and kinematics of vehicles were used to calculate the position of potential angle collision points between lane change vehicle and obstacle vehicles. Then, actual distance model was constructed based on potential angular collision points. The minimum safety distance model was also established under the two most unfavorable conditions. In order to achieve the lane change warning, three early warning rules were formulated. We verified the validity of models and early warning rules using vehicle driving video data of Interstate 80 in California. Models and early warning rules constructed in our research can be applied to the advanced active safety systems of vehicle, such as vehicle lane change assist system and active collision early warning system, which can improve the active safety and reduce traffic accidents.

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Collaborative approach for a safe driving distance using stereoscopic image processing

Cited by 18 publications

References 3 publications

Empirical Analysis of Safe Distance Calculation by the Stereoscopic Capturing and Processing of Images Through the Tailigator System

Empirical Analysis of Safe Distance Calculation by the Stereoscopic Capturing and Processing of Images Through the Tailigator System

ATRIP: Architecture for Traffic Classification Based on Image Processing

Research on Safety Lane Change Warning Method Based on Potential Angle Collision Point

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