A Novel Algorithm for Lane Detection based on Iterative Tree Search

Modern road vehicles are employing features of driver assistance systems (DAS) to improve drivability performance, comfort, and safety. In the future perspective, the advances in this field will lead these systems to the level of autonomous and cooperative driving, based on sensors networks and sensor fusion. This paper aims to present the readers a novel strategy for lane detection and tracking, which fits as a functional requirement to deploy DAS features like Lane Departure Warning and Lane Keeping Assist. To achieve the presented results, the digital image processing was divided into three levels. At the low-level, the input image dimensionality is reduced from three to one layer, the sharpness is improved, and region of interest is defined based on the minimum safe distance from the vehicle ahead. The feature extractor for lane edges detection design is part of the mid-level processing. The lane tracking strategy development is discussed in the high-level stage; Hough Transform and a shape-preserving spline interpolation are used to achieve a smooth lane fitting. The experimental outcomes were qualitatively and quantitatively evaluated using a ground truth comparison. The strategy shows good accuracy levels, including scenarios with shadows, curves, and road slope.

In recent years, many companies are working on the development of autonomous cars. Every citizen on the planet is concerned about their safety. In the framework of Advanced Driver Assistance Systems, one of the main goals is to improve safety and reduce road accidents, ultimately saving lives. One of the most difficult jobs in an autonomous driving system is detecting road lanes or road boundaries. Lane and object detection may be a crucial component of collision prevention in driving assistance systems. With the growth in traffic, there is a demand for more security and comfort when driving, it impose the development of new technology. Computer vision is one of the ways that may be utilized to assist the driver in difficult scenarios in order to improve his safety and comfort. The initial layer of autonomous vehicles' capabilities is lane tracking. Many sensors, including as lasers, radar, and vision sensors, are commonly employed for obstacle and lane detection. Computer vision is one of the primary method for detecting road limits and lanes using a vehicle's vision system. The technology uses a camera installed on the vehicle to capture the front view, then uses a few algorithms to detect lanes and objects. The lanes and objects are detected using a flexible algorithm. This paper focuses on a computer vision-based object detection technique by using CNN algorithm.

As the high growth of the number of vehicles, the traffic accidents are becoming more and more serious in recent years. In order to avoid the drivers being in danger, an intelligent vision-based system should focus on the image contents of the front the camera setting under the rear-view mirror in the vehicle. In this paper, we present a functional-link-based neuro-fuzzy network (FLNFN) structure for lane detection and departure warning system application. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. The lane detection method and the departure warning system proposed in this paper have been successfully evaluated on a PC platform of 3.2-GHz CPU, where the average frame-rate is up to 30fps.

Lane departure warning system plays an important role in driver assistance systems. The proposed algorithm assumes that lanes are always the straight lines and whole algorithm is based on Hough transform. Due to the complexity of urban traffic scenes, false lane detections are highly caused by warning lines and signs whose shapes and colors are similar to the lane boundary. In this study, we improve the accuracy of the lane detection base on Hough, a score function based on the width between left and right lanes is proposed to obtain reliable lane detect results on urban traffic scene. Meanwhile, a list of candidate lanes is constructed at the least of execution time. Experiments under various scenes showed that the proposed lane detection method can work robustly in the real-time.KeywordsLane detectionIntelligent vehicleMachine visiondriving assistance

Since the 1990s, there have been various lane detection systems designed to suit various road conditions such as highways, urban and rural roads. Current reserach has shown to predominantly detect only 1 lane marking set in real time and is unable to provide additional lanes for support in situations such as lane closures, road work conditions and car accidents that may obstruct the driving lane. These driver assistance systems are limited in their ability to assist the driver in these conditions. In this paper we propose a method to determine the markings of 2 lanes which can be used in conjunction with a detection system to detect obstacles present in front of the driver on the road. We first determine a suitable threshold for the perspective image to extract these road markings and signs, then apply morphological transformations to counter possible ‘deviations’ that may arise in this feature extraction technique. This method provides a robust approach to lane detection and works considerably well in various weather conditions. The resulting images show that the method developed can be used for lane-departure warning, as well as for obsatcle detection, in driver assistance.

In driving assistance systems, lane detection can provide significant information for driving safety. In this paper, we proposed a novel real-time lane detection method to extract the location of lane marking lines based on inverse perspective mapping transform (top view) for the region of interest (ROI) of a video frame. The data were then filtered by a selective oriented Gaussian high pass filter, Hough transformation, and Kalman filter to give the initial regions to our optimized RANSAC (Random Sample Consensus) Bezier splines fitting algorithm, which is the main innovation in this paper. Our experimental results and accuracy evaluation indicated that the proposed lane detection algorithm could run robustly in real time, and could achieve an average speed of 32.32 ms per frame for a 320 x 240 pixel image and 41.64 ms for a 640 x 480 pixel image, with a correct detection rate of over 92.85%. Moreover, our method was found to be suitable for various conditions.

The objective of this research is to develop an advanced driver assistance system with lane departure warning and forward collision warning functions. The main input of this system is a CMOS camera, which is used to acquire roadway image in front of vehicle. In order to extract lane markings and vehicles from roadway image, the image processing methods such as coordinate systems transformation, object detection and object tracking are applied to recognize the lane boundaries and the preceding vehicles. In lane marking recognition, gray scale statistics, dynamic range of interesting (ROI) and featured-based approaches are used to detect lane boundaries successfully. For vehicle recognition, a Sobel edge-enhancement filter and optical flow algorithm are used to implement vehicle detection and tracking. Besides, by means of the roadway and headway physical estimation models, the circumstances of lane departure and forward collision can be detected. The experimental results indicate that the hardware and the implemented algorithm used in this research are able to recognize the lane markings and headway distance precisely, and meet the requirements of real-time computing and high reliability.

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.

LuminanceGAN: Controlling the brightness of generated images for various night conditions

Using multisensory signals in advanced driver assistance is continuously increasing as a way to increase the attention and reduce the reaction time. It is essential that driver assistance system is capable of providing directional cues to the driver to direct his attention to the sides of the car as usually the focus is in front of the car. These signals could be for blind spot information, navigation, lane departure warning, collision warning, etc. This study investigated the effect of auditory and vibrotactile on directional attention in driver assistance systems. Moreover, two types of immersive displays were used in the driving simulation, namely the Head Mounted Display (HMD) and CAVE display, to study the effect of the type of display on the human performance. Lane Chang Task was used to assess the attention by measuring the response time to directional information. Vibrotactile and Auditory cues induced equal response times, meanwhile, vibrotactile signal was significantly gained higher satisfaction than auditory.

Lane detection is crucial for driver assistance systems. However, road scenes are severely degraded in dense fog, which leads to the loss of robustness of many lane detection methods. For this problem, an end-to-end method combining polarimetric dehazing and lane detection is proposed in this paper. From images with dense fog captured by a vehicle-mounted monochrome polarization camera, the darkest and brightest images are synthesized. Then, the airlight degree of polarization is estimated from angle of polarization, and the airlight is optimized by guided filtering to facilitate lane detection. After dehazing, the lane detection is carried out by a Canny operator and Hough transform. Having helped achieve good lane detection results in dense fog, the proposed dehazing method is also adaptive and computationally efficient. In general, this paper provides a valuable reference for driving safety in dense fog.

An advanced driver-assistance system (ADAS), based on lane detection technology, detects dangerous situations through various sensors and either warns the driver or takes over direct control of the vehicle. At present, cameras are commonly used for lane detection; however, their performance varies widely depending on the lighting conditions. Consequently, many studies have focused on using radar for lane detection. However, when using radar, it is difficult to distinguish between the plain road surface and painted lane markers, necessitating the use of radar reflectors for guidance. Previous studies have used long-range radars which may receive interference signals from various objects, including other vehicles, pedestrians, and buildings, thereby hampering lane detection. Therefore, we propose a lane detection method that uses an impulse radio ultra-wideband radar with high-range resolution and metal lane markers installed at regular intervals on the road. Lane detection and departure is realized upon using the periodically reflected signals as well as vehicle speed data as inputs. For verification, a field test was conducted by attaching radar to a vehicle and installing metal lane markers on the road. Experimental scenarios were established by varying the position and movement of the vehicle, and it was demonstrated that the proposed method enables lane detection based on the data measured.

This paper proposes an overview of achievements in the safety benefits (lives extended and injuries mitigated) provided by current or close-to-market vehicle technologies. It also proposes to review some of the research and developments in future vehicle safety such as connected vehicles and automated driving. It finally discusses expected safety benefits, and potentials as well as barriers to a large dissemination of these current and future technology-based traffic safety measures. As they are mostly foreseen in high income countries in the middle term, another type of revolution is needed in low and middle income countries for before these technologies can be put onto all global markets at a reasonable cost and contribute to the safety benefits of all road users. Frugal safety, coming from the frugal engineering concept, would be the recommended way to enable and accelerate this deployment. As for vehicle safety technologies already on the market, they have proven to be highly effective in mitigating vehicle occupant injuries, and injuries of vulnerable road users. Secondary safety devices and basic driver assistance systems have been largely evaluated and the effectiveness of seat belts, load limiters, pretensioners, airbags, car structure, electronic stability control (ESC), emergency brake assist (EBA) and other such devices have been examined. Available results show that, for example, if all cars were Euro NCAP five stars and fitted with EBA and ESC, compared to four stars without ESC and EBA, injury accidents would be reduced by 47%, all injuries would be mitigated by 68% and severe + fatal injuries by 70% (Page et al. in 53th AAAM conference, Baltimore, 2009). Unlike secondary safety systems, ESC, ultrasonic park assist, cornering lights and manual speed limiters, most of the primary safety systems or advanced assistance systems (such as blind spot detection, lane departure warning, lane keeping assist, automated cruise control, night vision and high speed AEB) are not, or just poorly, deployed on the market. There are a few reasons for this. First, their maturity and their capacity to work well without too many counter effects are recent, technological barriers have been strong for a long time. They were launched first in luxury cars in the USA, Japan and Western Europe in the mid-2000s and democratization has started just a few years back. Secondly, these features are costly, in terms of unit price and also in terms of investment; and their deployment demands progress in cost reduction. Thirdly, their expected safety benefits are, to a certain extent, not really known. If it is now epidemiologically demonstrated that secondary safety features bring safety, as well as ESC, the positive effectiveness of not yet largely deployed primary safety devices is only estimated and not yet proven, except for some of them like low speed automatic emergency braking (AEB). Studies about effectiveness of these devices take into account the potential in saving lives of generic functions, but a lot is still unknown about the real-world usage and acceptance by drivers and pedestrians of these applications. As there are many variants of these features, and especially concerning the mode of restitution to the driver of the information, the alert, or the warnings, the genuine efficiency of each variant is still a mystery. Of course, in-house studies by suppliers and car manufacturers anticipate positive acceptance by drivers and have identified and countered possible counter effects. Of course, current research is evaluating their potential to safety and in covering real-world driver needs, but a large field of research is still open for their safety assessment in real-life. Anyhow, the introduction of vehicle safety technologies is supposed to bring safety for a variety of road users and a variety of crash configurations. Of course, they will first be disseminated in high income countries but they will also be further disseminated, expectedly quickly, in the rest of the world under the pressure of customer demand, competition between vehicle makers as well as pressure from all kinds of New Car Assessment Programs, NCAP’s (Latin NCAP in South America, Bharat NCAP in India, Asian NCAP in Asia, etc.), and more generally from Global NCAP, which federates NCAP’s all over the world. This will be a first step in the globalization of existing or close-to-development vehicle safety technologies. Now, further on, emerging (more complex) technologies such as connectivity (vehicle-to-X communications) and automated driving will bring additional opportunities to tackle road crashes by adding safety applications not yet covered by passive safety or traditional (advanced) assistance systems. Actually connected technologies for safety and driving assistance systems in the current world of stand-alone technologies have three additional values:

The Driving Assistance Systems (DAS) aim to help the vehicle drivers to proceed through different road situations. However, their main task is not only to safe one particular driver, but also to increase the safety for all traffic members. The problem domain here is huge and might be divided into the subtopics, like driverpsilas fatigue detection, pedestrian tracking, obstacle collision avoidance, lane departure warnings and traffic signs detection and recognition. Advanced computer vision techniques are widely used in order to develop sufficient and robust systems for driving assistance. In this paper we discuss the video-based Hough-Transform driven objects detection algorithms and their applications for lane departure warnings, as well as for traffic signs detection. Furthermore, a high-speed hardware implementation of these algorithms on the FPGA/ASIC is also presented.

PurposeAdvanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.Design/methodology/approachA random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.FindingsThe acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.Originality/valueFew research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.