Multi-lane visual perception for lane departure warning systems (original) (raw)

An Improved Lane Detection and Tracking Method for Lane Departure Warning Systems

International Journal of Computer Vision and Image Processing, 2013

Lane detection and tracking are very crucial treatments in lane departure warning systems as they help the vehicle-mounted system to keep its lane. In this context, the authors’ work aims to develop vision-based lane detection and tracking method to detect and track lane limits in highways and main roads. The authors’ contribution focuses on the detection step. By exploiting the fact that, in an image, the road can be formed by linear and curvilinear portions, the authors propose two types of appropriate treatments to detect the lane limits. The authors’ method offers high precision rates independently of the painted lane marking’s characteristics, of the time of acquisition and in different weather conditions. Besides the challenges it overcomes, the authors’ method has the advantage of operating with a timing complexity that is reasonable for real-time applications. As shown experimentally, compared to three leading methods from the literature, the authors’ method has a higher eff...

Vision-based lane departure warning framework

Heliyon

Collisions arising from lane departures have contributed to traffic accidents causing millions of injuries and tens of thousands of casualties per year worldwide. Many related studies had shown that single vehicle lane departure crashes accounted largely in road traffic deaths that results from drifting out of the roadway. Hence, automotive safety has becoming a concern for the road users as most of the road casualties occurred due to driver's fallacious judgement of vehicle path. This paper proposes a vision-based lane departure warning framework for lane departure detection under daytime and night-time driving environments. The traffic flow and conditions of the road surface for both urban roads and highways in the city of Malacca are analysed in terms of lane detection rate and false positive rate. The proposed vision-based lane departure warning framework includes lane detection followed by a computation of a lateral offset ratio. The lane detection is composed of two stages: pre-processing and detection. In the pre-processing, a colour space conversion, region of interest extraction, and lane marking segmentation are carried out. In the subsequent detection stage, Hough transform is used to detect lanes. Lastly, the lateral offset ratio is computed to yield a lane departure warning based on the detected X-coordinates of the bottom end-points of each lane boundary in the image plane. For lane detection and lane departure detection performance evaluation, real-life datasets for both urban roads and highways in daytime and night-time driving environments, traffic flows, and road surface conditions are considered. The experimental results show that the proposed framework yields satisfactory results. On average, detection rates of 94.71% for lane detection rate and 81.18% for lane departure detection rate were achieved using the proposed frameworks. In addition, benchmark lane marking segmentation methods and Caltech lanes dataset were also considered for comparison evaluation in lane detection. Challenges to lane detection and lane departure detection such as worn lane markings, low illumination, arrow signs, and occluded lane markings are highlighted as the contributors to the false positive rates.

Real-time lane departure warning with cascade lane segmentation

The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 2023

Lane departure warning (LDW) is one of the safety innovations in autonomous cars that provides vehicle position monitoring. This technology will alarm if the vehicle moves out of the lane. Lane detection and lane measurement is the main part of LDW. The novelty of this research is the method can measure different lane marks. Very important to know the lane mark that can and cannot be passed. We use semantic segmentation to segment lane mark solid, lane mark dashed, and road. After extracting road and lane marks, we use inverse perspective mapping (IPM) to help calculate the measurement between the car and lane mark. The data is that 374 images were collected from several roads in Makassar City. The model was evaluated using intersection over union (IoU), reaching 79.8% accuracy. The developed system also estimates the measure between the vehicle and lane marks. The lane measurement estimation system's test results were evaluated using the root mean square error (RMSE) method to reach between 0.025254 and 0.134345.

Automotive standards-grade lane departure warning system

The increasing trend towards the use of image sensors in transportation is driven both by legislation and consumer demands for higher safety and a better-driving experience. Awareness of the environment that surrounds a vehicle can make driving and manoeuvring of the vehicle much safer for all road users. The authors present an image processing method to detect lane departure in forward-facing video specifically designed to be in accordance with proposed automotive lane departure warning standards. Our system uses a novel lane-marking segmentation algorithm in accordance with national standards for lane markings. This method does not demand the high computational requirements of inverse perspective mapping unlike methods proposed in related research. The authors present a method for lane boundary modelling based on subtractive clustering and Kalman filtering in the Hough transform domain, which is within the constraints of automotive standards. Finally, using the cameras intrinsic and extrinsic parameters, the width of the vehicle and guidelines issued by the International Organisation for Standardisation, we show how lane departure can be identified. Results are presented that verify the systems high detection rate and robustness to various background interference, lighting conditions and road environments.

Lane Departure Detection Using Geometrical and Intensity Patterns

A lane departure warning system (LDWS) is an essential part of an intelligent transportation system. This paper proposes a novel low-complexity LDWS that detects lane departures in video frames captured by smart phones with various lighting conditions and lane types and complicated road surfaces. The car used in the research was assumed to be traveling on a mostly straight road or highway signed with lane markings, and left and right lane markings were expected in fixed regions of frames. The Canny edge detector detected all the edges, allowing extraction of connected edge components. Left and right lane markings were selected from these components according to the position, orientation, and pixel intensity pattern. The presence or absence of lane markings in some consecutive frames was used to detect lane departure. This algorithm operated in real time and was successfully implemented on a tablet.

Design of Vision based Lane Departure Warning System

Journal of emerging technologies and innovative research, 2018

Road traffic accident is one of the problems that are risking lives of people. Most traffic accidents were caused by the negligence of the drivers. In order to reduce the number of traffic accidents and to improve the safety and efficiency of the traffic, Intelligent Transportation System (ITS) have been conducted worldwide .One of the component of ITS is Advanced driver assistance system has shown tremendous potential to address the on road measures taken for mitigating the fatalities due to distracted driving. The domain of Advanced Driver Assistance System covers Lane Departure Warning System (LDWS). In Every year, many car accidents mainly occurred around the world due to the lane Departure. Lane Departure Warning systems (LDWS) is one of the main approaches for Lane Detection and Lane Tracking and accident prevention. The Lane detection and Lane Tracking is a complicated problem in LDWS. Lane detection and Lane Tracking is a challenging task. But detection of Lane is not only u...

Lane Departure Detection Using Image Processing Techniques

—Video sensors constitute a great innovation in the automotive sector and road safety as they contribute to the development of driver assistance systems. These video systems use image processing techniques to inform drivers of impending dangers. One such development is the Lane Departure Warning System (LDWS) which play a key role in the prevention from accidents. The main function of this system is the detection of lane boundary lines using artificial vision. In this paper, we present a feature-based method for lane detection. We simplify the process of edge detection by using a horizontal differencing filter. The detected edge points are grouped into lines with a modified Hough transform.

Lane Detection and Lane Departure Warning System

International Journal of Advanced Trends in Computer Science and Engineering, 2021

Lane detection is important for autonomous vehicles. For this reason, many approaches use lane boundary information to locate the vehicle inside the street, or to integrate GPS-based localization. Advanced driver assistance systems are developed to assist drivers in the driving process reducing road accidents. In this work, we present an end-to-end system for lane identification, clustering and classification, based on two cascaded neural networks, that runs in real-time. The first step is camera calibration which is used to remove the effect of lens distortion. Then a canny edge detection algorithm finds the edges of the images. Then the region of interest (ROI) is selected. The ROI is actually based on the rectangular shape appearing at the bottom of the image. ROI removes the unwanted region in the image. The potential lane markers are then determined using the Hough transform to analyze lane boundaries. Once the lane pixels are found, these pixels are continuously scanned to obtain the best linear regression analysis. It is qualified to be applied on highways and urban roadways. It also has been successfully verified in sunny, and rainy conditions for both day and night.

VISION BASED DEPARTURE WARNING FOR LANE KEEPING ASSISTANCE SYSTEM

IJCIRAS, 2019

The increasing amount of vehicle usage leads in traffic accidents and most lane departure crashes occur especially on highway roads due to driver's inattention or incompetence or drowsiness. Therefore lane keeping assistance system is needed to save the considerable number of lives by warning the driver from imminent danger. Reducing the possibility of traffic accident occurrence is important for both developed and developing country. Moreover, lane departure warning is one of the most interesting parts in traffic safety system which still needs to develop. Most systems are based on computer vision and image processing with the use of visible-light cameras. Therefore, vision based image processing techniques are used in implementation of the system. This system is aimed to alerts the driver when the vehicle begins to drift out of its lane markings and road edges. In this system lanes are detected using improved Hough Transfrom. By testing with different datasets under various road conditions, results show that the proposed system has accuracy more than 99% on straight lanes.

Lane following and lane departure using a linear-parabolic model

Image and Vision Computing, 2005

This paper proposes a technique for unwanted lane departure detection. Initially, lane boundaries are detected using a combination of the edge distribution function and a modified Hough transform. In the tracking stage, a linear-parabolic lane model is used: in the near vision field, a linear model is used to obtain robust information about lane orientation; in the far field, a quadratic function is used, so that curved parts of the road can be efficiently tracked. For lane departure detection, orientations of both lane boundaries are used to compute a lane departure measure at each frame, and an alarm is triggered when such measure exceeds a threshold. Experimental results indicate that the proposed system can fit lane boundaries in the presence of several image artifacts, such as sparse shadows, lighting changes and bad conditions of road painting, being able to detect in advance involuntary lane crossings. q