Journal ArticleDOI
A lane detection approach based on intelligent vision
Reads0
Chats0
TLDR
A modified approach is proposed to accelerate the HT process in a computationally efficient manner, thereby making it suitable for real-time lane detection and lane departure prediction.About:
This article is published in Computers & Electrical Engineering.The article was published on 2015-02-01. It has received 58 citations till now. The article focuses on the topics: Image processing.read more
Citations
More filters
Journal ArticleDOI
A review of recent advances in lane detection and departure warning system
TL;DR: An overview of current LDW system is provided, describing in particular pre-processing, lane models, lane de Ntection techniques and departure warning system.
Journal ArticleDOI
A lane-change trajectory model from drivers’ vision view
TL;DR: Results show the proposed lane-change trajectory model can successfully describe Drivers’ lane-changing trajectories, and some parameters in the model are directly associated to drivers’ driving characteristics during lane- change.
Journal ArticleDOI
Lane detection technique based on perspective transformation and histogram analysis for self-driving cars
TL;DR: This study focuses on demonstrating a powerful end-to-end lane detection method using contemporary computer vision techniques for self-driving cars and proposes an improved lane detection technique based on perspective transformations and histogram analysis.
Journal ArticleDOI
Optimal control of intelligent vehicle longitudinal dynamics via hybrid model predictive control
TL;DR: The intelligent vehicle longitudinal dynamics is approximated as a two-mode discrete-time mixed logical dynamical (MLD) system and a hybrid model predictive controller, which allows to optimize the switching sequences of the operation modes and the torques acted on the wheels, is tuned based on online mixed-integer quadratic programming.
Journal ArticleDOI
Embedded real-time speed limit sign recognition using image processing and machine learning techniques
Samuel Luz Gomes,Elizângela de S. Rebouças,Edson Cavalcanti Neto,João Paulo Papa,Victor Hugo C. de Albuquerque,Pedro Pedrosa Rebouças Filho,João Manuel R. S. Tavares +6 more
TL;DR: The detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features based on the optimum-path forest classifier, support vector machines, multilayer perceptron, k-nearest neighbor, extreme learning machine, least mean squares, and least squares machine learning techniques are proposed.
References
More filters
Journal ArticleDOI
Vehicle and Guard Rail Detection Using Radar and Vision Data Fusion
TL;DR: In this article, a vehicle detection system fusing radar and vision data is described, where the radar data is used to locate areas of interest on images and vehicle search in these areas is mainly based on vertical symmetry.
Journal ArticleDOI
A vehicle license plate detection method using region and edge based methods
TL;DR: A filtering method called ''region-based'' is proposed in order to smooth the uniform and background areas of an image, the Sobel operator and morphological filtering to extract the vertical edges and the candidate regions respectively, and the plate region is segmented by considering some geometrical features.
Journal ArticleDOI
Applying a Functional Neurofuzzy Network to Real-Time Lane Detection and Front-Vehicle Distance Measurement
TL;DR: A real-time lane-detection and front-vehicle distance measurement system that uses a mounted camera inside a vehicle has been designed for safe driving and the experimental results show that the system works successfully in real- time environment.
Journal ArticleDOI
Parallelizing the Hough Transform Computation
TL;DR: A method called additive Hough transform (AHT) is proposed, based on parallel processing of k2 points, obtained by dividing the edge map into uniform blocks using a k times k grid, which reduces the total computation time by at least k2 times as compared to existing Houghtransform architectures.