Probabilistic lane detection and lane tracking for autonomous vehicles using a cascade particle filter

2015 ◽  
Vol 229 (12) ◽  
pp. 1656-1671 ◽  
Author(s):  
Minchae Lee ◽  
Chulhoon Jang ◽  
Myoungho Sunwoo
Keyword(s):  
Particle Filter ◽  
Autonomous Vehicles ◽  
Lane Detection ◽  
Lane Tracking

FLane: An Adaptive Fuzzy Logic Lane Tracking System for Driver Assistance

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
Giulio Reina ◽  
Annalisa Milella
Keyword(s):  
Fuzzy Logic ◽  
Autonomous Vehicles ◽  
Tracking System ◽  
A Priori ◽  
Critical Role ◽  
Lane Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Lane Tracking ◽  
Lane Position

In the last few years, driver assistance systems are increasingly being investigated in the automotive field to provide a higher degree of safety and comfort. Lane position determination plays a critical role toward the development of autonomous and computer-aided driving. This paper presents an accurate and robust method for detecting road markings with applications to autonomous vehicles and driver support. Much like other lane detection systems, ours is based on computer vision and Hough transform. The proposed approach, however, is unique in that it uses fuzzy reasoning to combine adaptively geometrical and intensity information of the scene in order to handle varying driving and environmental conditions. Since our system uses fuzzy logic operations for lane detection and tracking, we call it “FLane.” This paper also presents a method for building the initial lane model in real time, during vehicle motion, and without any a priori information. Details of the main components of the FLane system are presented along with experimental results obtained in the field under different lighting and road conditions.

Real-time lane tracking using Rao-Blackwellized particle filter

2012 ◽  
Vol 11 (1) ◽  
pp. 179-191 ◽  
Author(s):  
Marcos Nieto ◽  
Andoni Cortés ◽  
Oihana Otaegui ◽  
Jon Arróspide ◽  
Luis Salgado
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Lane Tracking

A Fuzzy Lane Tracking System for Driver Assistance

2006 ◽  
Author(s):  
Annalisa Milella ◽  
Giulio Reina
Keyword(s):  
Fuzzy Logic ◽  
Autonomous Vehicles ◽  
Tracking System ◽  
Critical Role ◽  
Lane Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Inference System ◽  
Driver Support Systems ◽  
Lane Position

In the last few years, driver-assistance systems are increasingly being investigated in automotive field to provide a higher degree of comfort and safety. Lane position determination plays a critical role toward the development of autonomous and computer-aided driving. This paper presents an accurate and robust method for detecting lateral road marking with applications in autonomous vehicles and driver support systems. Much like other lane detection systems, ours is based on computer vision and Hough transform. Our approach, however, is unique in that it combines geometrical and intensity information of the image, based on a fuzzy logic inference system implementing in-depth understanding of different driving and environmental conditions. We call it Fuzzy Logic lane (FLane) tracking system. Details of the main components of the FLane module are presented along with experimental results obtained under varying lighting and road conditions. It is shown that the proposed method is reliable and effective in detecting road border and can be successfully employed for driver assistance.

FAST LANE DETECTION USING DIRECTION KERNEL FUNCTION

2012 ◽  
Vol 10 (02) ◽  
pp. 1250017 ◽  
Author(s):  
YIMING NIE ◽  
BIN DAI ◽  
XIANGJING AN ◽  
ZHENPING SUN ◽  
TAO WU ◽  
...  
Keyword(s):  
Kernel Function ◽  
Lane Detection ◽  
Vanishing Point ◽  
Robust Detection ◽  
The Road ◽  
Road Lighting ◽  
Lane Tracking ◽  
Lane Marking ◽  
On The Road ◽  
Fast Lane

The lane information is essential to the highway intelligent vehicle applications. The direct description of the lanes is lane markings. Many vision methods have been proposed for lane markings detection. But in practice there are some problems to be solved by previous lane tracking systems such as shadows on the road, lighting changes, characters on the road and discontinuous changes in road types. Direction kernel function is proposed for robust detection of the lanes. This method focuses on selecting points on the markings edge by classification. During the classifying, the vanishing point is selected and the parts of the lane marking could form the lanes. The algorithm presented in this paper is proved to be both robust and fast by a large amount of experiments in variable occasions, besides, the algorithm can extract the lanes even in some parts of lane markings missing occasions.

scholarly journals Lane Detection for Autonomous Vehicles using Open CV Library

2020 ◽  
Vol 6 (12) ◽  
pp. 176-180
Author(s):  
Aarushi Mittal and Narinder Kaur
Keyword(s):  
Hough Transform ◽  
Autonomous Vehicles ◽  
Region Of Interest ◽  
Traffic Signals ◽  
Lane Detection ◽  
Polar Coordinates ◽  
Cartesian Coordinates ◽  
Path Discovery

For vehicles to have the option to drive without anyone else, they have to comprehend their encompassing world like human drivers, so they can explore their way in roads, pause at stop signs and traffic signals, and try not to hit impediments, for example, different vehicles and pedestrians. In view of the issues experienced in identifying objects via self-governing vehicles an exertion has been made to show path discovery utilizing OpenCV library. The explanation and method for picking grayscale rather than shading, distinguishing and detecting edges in an image, selecting region of interest, applying Hough Transform and choosing polar coordinates over Cartesian coordinates has been discussed.

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