Intervehicle distance estimation through camera images

Lee, Jaemyoung

doi:10.1117/1.jei.27.6.063001

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Lee [17] focused on inter-vehicle distance estimate based on lane width. The proposed technique had a distance estimate error of less than 7%.…”

Section: Target Vehicle's Location Estimationmentioning

confidence: 99%

Geolocation estimation of target vehicles using image processing and geometric computation

Namazi¹,

Mester²,

Lu³

et al. 2022

Preprint

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Estimating vehicles' locations is one of the key components in intelligent traffic management systems (ITMSs) for increasing traffic scene awareness. Traditionally, stationary sensors have been employed in this regard. The development of advanced sensing and communication technologies on modern vehicles (MVs) makes it feasible to use such vehicles as mobile sensors to estimate the traffic data of observed vehicles. This study aims to explore the capabilities of a monocular camera mounted on an MV in order to estimate the geolocation of the observed vehicle in a global positioning system (GPS) coordinate system.We proposed a new methodology by integrating deep learning, image processing, and geometric computation to address the observed-vehicle localization problem. To evaluate our proposed methodology, we developed new algorithms and tested them using real-world traffic data. The results indicated that our proposed methodology and algorithms could effectively estimate the observed vehicle's latitude and longitude dynamically.

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“…Lee [17] focused on inter-vehicle distance estimate based on lane width. The proposed technique had a distance estimate error of less than 7%.…”

Section: Target Vehicle's Location Estimationmentioning

confidence: 99%

Geolocation estimation of target vehicles using image processing and geometric computation

Namazi¹,

Mester²,

Lu³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…To the best of our knowledge, no other paper has determined the number, type, distance, and speed of vehicles at the same time. Most of the papers have tried to extract one data type (e.g., [24], [25], [27], [30], [29], [31], and [32]), which is not enough for managing traffic safely and efficiently.…”

Section: Detect Vehiclementioning

confidence: 99%

“…This system uses a camera as a sensor and includes image processing, information collection, vanishing point detection, road region segmentation, and estimation of the inter-vehicle distance. Lee [29] presented a method for estimating the inter-vehicle distance using a blackbox camera. The idea is to estimate the distance based on the lane width for the detected vehicle.…”

Section: Introductionmentioning

confidence: 99%

Using Vehicle-Mounted Camera to Collect Information for Managing Mixed Traffic

Namazi

Holthe-Berg

Lofsberg

et al. 2019

2019 15th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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With increasingly rapid advances in the field of producing modern and autonomous vehicles, the need for intelligent traffic management systems, which take advantage of the vehicle's abilities to sense and communicate, has increased. A considerable amount of literature has been published on managing traffic that includes only autonomous vehicles. However, changing all vehicles to autonomous versions is a longterm process. In the near future, traffic will be a mixture of human-driven and autonomous vehicles. To date, few studies have investigated mixed traffic in intelligent management systems. The main objective of this research is to study the possibility of using a vehicle-mounted camera to sense and collect the required traffic data of the surrounding vehicles in mixed traffic. To achieve this, a vehicle with a monocular camera is used to collect image information for detecting and counting the vehicles in different lanes and estimating their distance and speed on the defined route. The results indicate that our proposed image processing algorithms can acquire the information needed for intelligent traffic management systems.

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“…Measuring the surrounding vehicles’ longitudinal and lateral distances can provide safe distance control during driving. 1,2…”

Section: Introductionmentioning

confidence: 99%

Inter-vehicle distance estimation considering camera attitude angles based on monocular vision

Liu

Zhang

Hou

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Perceiving the distance between vehicles is a crucial issue for advanced driving assistance systems. However, most vision-based distance estimation methods do not consider the influence of the change in camera attitude angles during driving or only use the vanishing point detected by lane lines to correct the pitch angle. This paper proposed an improved pinhole distance estimation model based on the road vanishing point without the lane line information. First, the road vanishing point is detected based on the dominant texture orientation, and the yaw and pitch angles of the camera are estimated. Then, a distance estimation model considering attitude angle compensation is established. Finally, the experimental results show that the proposed method can effectively correct the influence of the camera attitude angle on the distance estimation results.

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Intervehicle distance estimation through camera images

Cited by 4 publications

References 8 publications

Geolocation estimation of target vehicles using image processing and geometric computation

Geolocation estimation of target vehicles using image processing and geometric computation

Using Vehicle-Mounted Camera to Collect Information for Managing Mixed Traffic

Inter-vehicle distance estimation considering camera attitude angles based on monocular vision

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