Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation:A review

Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning is whether it can accurately and quickly detect lane lines.Since 1990 s,they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road.After then,the accuracy for particular situations,the robustness for a wide range of scenarios,time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject.At present,these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories:the traditional image processing and semantic segmentation(includes deep learning)methods.The former mainly involves feature-based and model-based steps,and which can be classified into similarity-and discontinuity-based ones;and the model-based step includes different parametric straight line,curve or pattern models.The semantic segmentation includes different machine learning,neural network and deep learning methods,which is the new trend for the research and application of lane line departure warning systems.This paper describes and analyzes the lane line departure warning systems,image processing algorithms and semantic segmentation methods for lane line detection.

The analysis of lane detection algorithms using histogram shapes and Hough transform

Purpose–The purpose of this paper is to develop a lane detection analysis algorithm by Hough transform and histogram shapes,which can effectively detect the lane markers in various lane road conditions,in driving system for drivers.Design/methodology/approach–Step 1:receiving image:the developed system is able to acquire images from video files.Step 2:splitting image:the system analyzes the splitting process of video file.Step 3:cropping image:specifying the area of interest using crop tool.Step 4:image enhancement:the system conducts the frame to convert RGB color image into grayscale image.Step 5:converting grayscale image to binary image.Step 6:segmenting and removing objects:using the opening morphological operations.Step 7:defining the analyzed area within the image using the Hough transform.Step 8:computing Houghline transform:the system operates the defined segment to analyze the Houghline transform.Findings–This paper presents the useful solution for lane detection by analyzing histogram shapes and Hough transform algorithms through digital image processing.The method has tested on video sequences filmed by using a webcam camera to record the road as a video file in a form of avi.The experimental results show the combination of two algorithms to compare the similarities and differences between histogram and Hough transform algorithm for better lane detection results.The performance of the Hough transform is better than the histogram shapes.Originality/value–This paper proposed two algorithms by comparing the similarities and differences between histogram shapes and Hough transform algorithm.The concept of this paper is to analyze between algorithms,provide a process of lane detection and search for the algorithm that has the better lane detection results.

Evaluating effectiveness and acceptance of advanced driving assistance systems using field operational test

A large number of reported road collisions are caused by driver inattention,and inappropriate driving behaviour.This study investigated the effectiveness and acceptance of Advanced Driving Assistance Systems(ADAS)for driver age groups,gender,occupation(professional/non-professional),and road type(expressway,urban roads,and semi-urban road)based on the Field Operational Test(FOT).The ADAS is provided with assistance features,such as Lane Departure Warning(LDW),Forward Collision Warning(FCW),and Traffic Speed Recognition Warning(TSRW).In total,the FOT involved 30 participants who drove the test vehicle twice(once in the stealth phase and once in the active phase).The FOT included three sections:expressway(20.60 km),urban road(7.2 km),and semi-urban road(13.35 km).A questionnaire was used to determine user acceptance of the ADAS technology.In addition,parametric and non-parametric statistical tests were carried out to determine ADAS's significant effects.The FOT results showed statistically significant differences in the LDW’s acceptance and effectiveness for gender,age group,occupation,and road type before and after exposure to ADAS.Male participants showed significant lateral behavior improvement compared to female participants.Old-aged drivers scored the highest acceptance score for the technology compared to middle and young-aged drivers.The subjective ratings ranked the assistance features in descending order as TSRW,LDW,and FCW.This study’s findings can support policy development and induce trust in the public for the technology adoption to improve road traffic safety.