Real-Time Lane Detection and Rear-End Collision Warning System on a Mobile Computing Platform

Tang, Samuel Jia Wei; Ng, Kok Yew; Khoo, Boon How; Parkkinen, Jussi

doi:10.1109/compsac.2015.171

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…Different other algorithms can be used for classifications and detection of cars i images. For example, in [35], a method combining temporal difference with and edge detector is used to detect cars. Such methods is widely proposed in the literature, and considered fast in terms of processing.…”

Section: A Visual Processing Of Traffic Informationmentioning

confidence: 99%

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An Innovative Smartphone-based Solution for Traffic Rule Violation Detection

Alasmary¹

2020

IJACSA

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This paper introduces a novel smartphone-based solution to detect different traffic rule violations using a variety of computer vision and networking technologies. We propose the use of smartphones as participatory sensors via their cameras to detect the moving and stationary objects (e.g., cars and lane markers) and understand the resulting driving and traffic violation of each object. We propose novel framework which uses a fast in-mobile traffic violation detector for rapid detection of traffic rule violation. After that, the smartphone transmits the data to the cloud where more powerful computer vision and machine learning operations are used to detect the traffic violation with a higher accuracy. We show that the proposed framework detection is very accurate by combining a) a Haarlike feature cascade detector at the in-mobile level, and b) a deep learning-based classifier, and support-vector machine-based classifiers in the cloud. The accuracy of the deep convolutional network is about 92% for true positive and 95% for true negative. The proposed framework demonstrates a potential for mobilebased traffic violation detection by especially by combining the information of accurate relative position and relative speed. Finally, we propose a real-time scheduling scheme in order to optimize the use of battery and real-time bandwidth of the users given partially known navigation information among the different users in the network, which us the real case. We show that the navigation information is very important in order to better utilize the battery and bandwidth for each user for a small number of users compared to the navigation trajectory length. That is, the utilization of the resources is directly related to the number of available participants, and the accuracy of navigation information.

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Section: A Visual Processing Of Traffic Informationmentioning

confidence: 99%

“…In [38], the authors used a combined IPM transformation and Hough transform line detector to detect the lanes. In [35], a modified Hough transform with a hardcoded search area is used for lane detection. A robust and fast line detector is proposed in [40] shows a potential to detect lines on roads.…”

Section: A Visual Processing Of Traffic Informationmentioning

confidence: 99%

An Innovative Smartphone-based Solution for Traffic Rule Violation Detection

Alasmary¹

2020

IJACSA

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“…ere are other object detection algorithms other than the Cascade classifier. A Canny edge detector with a temporal difference is used to detect vehicles in [13]. e results show that multiple vehicles can be detected in one frame as one segment, which is usually not desirable.…”

Section: Machine Learning For Object Detectionmentioning

confidence: 99%

An Innovative Autonomous and Semiautonomous Cars Communication Framework: An Environment Perception Perspective

Alasmary

2019

Journal of Computer Networks and Communications

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Autonomous vehicles are equipped with multiple sensors that allow perception of the road environment. However, there are always challenges in terms of measurement accuracy, dynamics of the road driving conditions, and extended perception availability. Vehicular communication technologies have already been extensively researched, and the IEEE 802.11p standard has been approved. Therefore, communications could help in extending the perception of the autonomous vehicles if proper information transmission mechanisms are utilized. In fact, this paper proposes a novel and innovative design that will allow vehicles to extend their perception by exchanging a smaller number of packets than needed and estimate the actual perception of the environment. First, we propose a novel MAC layer that is compatible with the IEEE 802.11p standard that allows vehicles to recover extra perceptional areas of the environment as they receive new packets. Second, we demonstrate that this approach will result in a better utilization of the communication channel and acceptable perception accuracy of the environment, compared to transmitting the complete information.

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“…The lanes are detected based on the processed image using the Hough transform. This technique limits the image processing speed and requires many memory blocks [ 6 , 7 ]. Therefore, the conventional lane detection method results in a high-power consumption when used in high-speed cars with high processing speeds [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

On-CMOS Image Sensor Processing for Lane Detection

Lee

Jeong

Park

et al. 2021

Sensors

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This paper presents a CMOS image sensor (CIS) with built-in lane detection computing circuits for automotive applications. We propose on-CIS processing with an edge detection mask used in the readout circuit of the conventional CIS structure for high-speed lane detection. Furthermore, the edge detection mask can detect the edges of slanting lanes to improve accuracy. A prototype of the proposed CIS was fabricated using a 110 nm CIS process. It has an image resolution of 160 (H) × 120 (V) and a frame rate of 113, and it occupies an area of 5900 μm × 5240 μm. A comparison of its lane detection accuracy with that of existing edge detection algorithms shows that it achieves an acceptable accuracy. Moreover, the total power consumption of the proposed CIS is 9.7 mW at pixel, analog, and digital supply voltages of 3.3, 3.3, and 1.5 V, respectively.

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Real-Time Lane Detection and Rear-End Collision Warning System on a Mobile Computing Platform

Cited by 14 publications

References 15 publications

An Innovative Smartphone-based Solution for Traffic Rule Violation Detection

An Innovative Smartphone-based Solution for Traffic Rule Violation Detection

An Innovative Autonomous and Semiautonomous Cars Communication Framework: An Environment Perception Perspective

On-CMOS Image Sensor Processing for Lane Detection

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