Driver Assistance System Based on Monocular Vision

Chiang, Yu-Min; Hsu, No-Zen; Lin, Kuan-Liang

doi:10.1007/978-3-540-69052-8_1

“…is the total amount of pixels from lowest frame capture to the detected object. Graph describes for towards real distance from formula (8) as shown in Figure 12 has a different value from Figure 11. The resulting figures are different but have a similar graphical shape because the input parameter values are the same.…”

Section: Experiments Resultsmentioning

confidence: 94%

Object Distance Measurement System Using Monocular Camera on Vehicle

Dirgantara¹,

Syaichu-Rohman²,

Yulianti³

2019

EECSI

0

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To support autonomous vehicles that are currently often studied by various parties, the authors propose to make a system of predicting the distance of objects using monocular cameras on vehicles. Distance prediction uses four methods and the input parameter was obtained from images processed with MobileNets SSD. Calculations using linear regression are the simplest calculations among the four methods but have an error of 1% with a standard deviation of 1.65 meters. While using the first method, the average error value is 9% with a standard deviation of 0.43 meters. By using the second calculation, the average error resulted in 6% with a standard deviation of 0.35 meters. The experimental method had an average error of 1% with a standard deviation of 0.26 meters, so the experimental method was used.

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“…The swiftness of the computational process is one of its huge advantages, since the system will be plugged on the real-time database. Steering for the autonomous vehicle would run smoothly to avoid object [8].…”

Section: A Mobilenets Ssdmentioning

confidence: 99%

“…Second method used for distance measurement is determined by combining the ratio between moving object pixels and the angle ratio, with the following equation [8]: 6. An Oblique object measurement calculation using second method (reproduced from reference [8]).…”

Section: Frame Based Distance Measurementmentioning

confidence: 99%

Object Distance Measurement System Using Monocular Camera on Vehicle

Dirgantara¹,

Syaichu-Rohman²,

Yulianti³

2019

EECSI

0

View full text Add to dashboard Cite

To support autonomous vehicles that are currently often studied by various parties, the authors propose to make a system of predicting the distance of objects using monocular cameras on vehicles. Distance prediction uses four methods and the input parameter was obtained from images processed with MobileNets SSD. Calculations using linear regression are the simplest calculations among the four methods but have an error of 1% with a standard deviation of 1.65 meters. While using the first method, the average error value is 9% with a standard deviation of 0.43 meters. By using the second calculation, the average error resulted in 6% with a standard deviation of 0.35 meters. The experimental method had an average error of 1% with a standard deviation of 0.26 meters, so the experimental method was used.

show abstract

“…The cameras serve a variety of purposes, from assisting drivers during parking and reversing to providing security footage. The integration of camera systems has promoted the development of advanced driver assistance systems, offering drivers warnings and supportive features for a safer driving experience [1]. The implementation of such systems significantly improves traffic safety, as human error continues to be the leading cause of driving accidents, as reported by the European Commission (2019,2020).…”

Section: Introductionmentioning

confidence: 99%