Real time detection of lane markers in urban streets

Aly, Mohamed

doi:10.1109/ivs.2008.4621152

Cited by 680 publications

(483 citation statements)

References 11 publications

(13 reference statements)

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“…It represents the canonical approach of defining the instant value of the steering angle in autonomous cars [23,24]. According to this model, the SAF that defines the steering angle δ is assumed to be the sum of the following two scaled parameters: the linear e and angular θ deviations of the car from its desired trajectory (e.g., the center of the lane) as illustrated in Figure 3.…”

Section: Servo Control As a Pd Controllermentioning

confidence: 99%

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Алексеева¹,

Tanev²,

Shimohara³

2018

Algorithms

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Abstract:The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions.

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Section: Servo Control As a Pd Controllermentioning

confidence: 99%

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Алексеева¹,

Tanev²,

Shimohara³

2018

Algorithms

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“…In urban areas and on highways, visual road detection is carried out by detection of lane markers. A common approach is to reproject the image onto the ground plane followed by line detection in the resampled image [1,6,7]. Using other sensors such as Lidar, similar lane detection approaches are used [5].…”

Section: Railway Vs Road Detectionmentioning

confidence: 99%

Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera

Berg

Öfjäll

Ahlberg

et al. 2015

Lecture Notes in Computer Science

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Abstract. We propose a method for detecting obstacles on the railway in front of a moving train using a monocular thermal camera. The problem is motivated by the large number of collisions between trains and various obstacles, resulting in reduced safety and high costs. The proposed method includes a novel way of detecting the rails in the imagery, as well as a way to detect anomalies on the railway. While the problem at a first glance looks similar to road and lane detection, which in the past has been a popular research topic, a closer look reveals that the problem at hand is previously unaddressed. As a consequence, relevant datasets are missing as well, and thus our contribution is two-fold: We propose an approach to the novel problem of obstacle detection on railways and we describe the acquisition of a novel data set.

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“…Hence, information about the lane (Aly, 2008;Bao et al, 2011) or geometric layout (López et al, 2010) are of interest. With the application in mind, it is known that the camera is placed on the side of the road and it is assumed that it is observing a fairly straight piece of a path/road.…”

Section: Reduced Search Space Using Prior Knowledgementioning

confidence: 99%

Reduced Search Space for Rapid Bicycle Detection

Ardö

Nilsson

Laureshyn³

et al. 2013

Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

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Abstract:This paper describes a solution to the application of rapid detection of bicycles in low resolution video. In particular, the application addressed is from video recorded in a live environment. The future aim from the results in this paper is to investigate a full year of video data. Hence, processing speed is of great concern.The proposed solution involves the use of an object detector and a search space reduction method based on prior knowledge regarding the application at hand. The method using prior knowledge utilizes random sample consensus, and additional statistical analysis on detection outputs, in order to define a reduced search space. It is experimentally shown that, in the application addressed, it is possible to reduce the full search space by 62% with the proposed methodology. This approach, which employs a full detector in combination with the design of a simple and fast model that can capture prior knowledge for a specific application, leads to a reduced search space and thereby a significantly improved processing speed.

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Real time detection of lane markers in urban streets

Cited by 680 publications

References 11 publications

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera

Reduced Search Space for Rapid Bicycle Detection

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