Stereo vision enabling fast estimation of free space on traffic roads for autonomous navigation

Lee, K. Y.; Song, Gwang-Yul; Park, J. M.; Lee, J. W.

doi:10.1007/s12239-015-0012-7

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…In this section, one of the navigation methods developed by category map building that scores navigation map. It is a novel algorithm that was developed from information derived from the stereo camera [17]. The aim is to estimate the free space of the vehicle (car) which is in front of where the system is run, particularly for navigation on the highway.…”

Section: Score Map Buildingmentioning

confidence: 99%

Review of Vision-Based Robot Navigation Method

Rahmani¹,

Putra

Harjoko

et al. 2015

IJRA

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Vision-based robot navigation is a research theme that continues to be developed up to now by the researchers in the field of robotics. There are innumerable methods or algorithms are developed, and this paper described the reviews of the methods. The methods are distinguished whether the robot is equipped with the navigation map (map-based), the map is built incrementally as robot observes the environment (map-building), or the robot navigates using no map (mapless). In this paper will described navigation methods of map-based, map-building, and mapless category.

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Section: Score Map Buildingmentioning

confidence: 99%

Review of Vision-Based Robot Navigation Method

Rahmani¹,

Putra

Harjoko

et al. 2015

IJRA

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“…For high level feature extraction techniques such as Haar Features [3] and HOG [4] are found to be more useful. They have shown good performances in the detection of Human [5][6][7] which is the most important feature in automatic decision-making system for self-driving vehicles [8,9] or autonomous [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Automatic decision making system with environmental and traffic data

Ersoy¹,

Waqar²,

Toptaş³

2021

Math. models, eng.

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By using computer vision and machine learning methods, driving lane detection and tracking, the position of the vehicles in the vicinity, their speed and direction will be determined through real-time processing of images taken from the traffic camera. Processing of the collected data using artificial intelligence and fuzzy logic and to calculate the data within the scope of "game theory" and to implement the dynamic control of the vehicle in the light of calculated data is planned. In addition to that, the designed system can also function as a driver assistant for non-autonomous vehicles with an appropriate user interface. First, the positions of the vehicles and driving lanes will be detected and monitored using computer vision and machine learning methods. Then, the vehicle speeds will be calculated by taking advantage of the historical data of the vehicle positions in the surrounding area from the previous observations, and the location estimation will be made by creating probability distributions of where each vehicle will be in the future. With the position estimation and the obtained speed information, it will be ensured that the vehicle is in the safest position in the transportation process to the destination and that it travels again at the safest speed.

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