Relevant bibliographies by topics / Detection of road lane lines

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Detection of road lane lines'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Detection of road lane lines"

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Jung, Jiyoung, and Sung-Ho Bae. "Real-Time Road Lane Detection in Urban Areas Using LiDAR Data." Electronics 7, no. 11 (October 26, 2018): 276. http://dx.doi.org/10.3390/electronics7110276.

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The generation of digital maps with lane-level resolution is rapidly becoming a necessity, as semi- or fully-autonomous driving vehicles are now commercially available. In this paper, we present a practical real-time working prototype for road lane detection using LiDAR data, which can be further extended to automatic lane-level map generation. Conventional lane detection methods are limited to simple road conditions and are not suitable for complex urban roads with various road signs on the ground. Given a 3D point cloud scanned by a 3D LiDAR sensor, we categorized the points of the drivable region and distinguished the points of the road signs on the ground. Then, we developed an expectation-maximization method to detect parallel lines and update the 3D line parameters in real time, as the probe vehicle equipped with the LiDAR sensor moved forward. The detected and recorded line parameters were integrated to build a lane-level digital map with the help of a GPS/INS sensor. The proposed system was tested to generate accurate lane-level maps of two complex urban routes. The experimental results showed that the proposed system was fast and practical in terms of effectively detecting road lines and generating lane-level maps.
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Hermosillo-Reynoso, Fernando, Deni Torres-Roman, Jayro Santiago-Paz, and Julio Ramirez-Pacheco. "A Novel Algorithm Based on the Pixel-Entropy for Automatic Detection of Number of Lanes, Lane Centers, and Lane Division Lines Formation." Entropy 20, no. 10 (September 21, 2018): 725. http://dx.doi.org/10.3390/e20100725.

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Lane detection for traffic surveillance in intelligent transportation systems is a challenge for vision-based systems. In this paper, a novel pixel-entropy based algorithm for the automatic detection of the number of lanes and their centers, as well as the formation of their division lines is proposed. Using as input a video from a static camera, each pixel behavior in the gray color space is modeled by a time series; then, for a time period τ , its histogram followed by its entropy are calculated. Three different types of theoretical pixel-entropy behaviors can be distinguished: (1) the pixel-entropy at the lane center shows a high value; (2) the pixel-entropy at the lane division line shows a low value; and (3) a pixel not belonging to the road has an entropy value close to zero. From the road video, several small rectangle areas are captured, each with only a few full rows of pixels. For each pixel of these areas, the entropy is calculated, then for each area or row an entropy curve is produced, which, when smoothed, has as many local maxima as lanes and one more local minima than lane division lines. For the purpose of testing, several real traffic scenarios under different weather conditions with other moving objects were used. However, these background objects, which are out of road, were filtered out. Our algorithm, compared to others based on trajectories of vehicles, shows the following advantages: (1) the lowest computational time for lane detection (only 32 s with a traffic flow of one vehicle/s per-lane); and (2) better results under high traffic flow with congestion and vehicle occlusion. Instead of detecting road markings, it forms lane-dividing lines. Here, the entropies of Shannon and Tsallis were used, but the entropy of Tsallis for a selected q of a finite set achieved the best results.
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He, Peng, and Feng Gao. "Study on Lane Detection Based on Computer Vision." Advanced Materials Research 765-767 (September 2013): 2229–32. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2229.

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Lane detection is a crucial component of automotive driver assistance system aiming to increase safety, convenience and efficiency of driving. This paper developed a vision based algorithm of detecting road lanes which is performed by extracting edges and finding straight lines using improved Hough transform. The experimental results indicate that this algorithm is effective and precise. Furthermore, this algorithm paves the way for the implementation of automotive driver assistance system.
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Farag, Wael. "Real-Time Detection of Road Lane-Lines for Autonomous Driving." Recent Advances in Computer Science and Communications 13, no. 2 (June 3, 2020): 265–74. http://dx.doi.org/10.2174/2213275912666190126095547.

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Background: Enabling fast and reliable lane-lines detection and tracking for advanced driving assistance systems and self-driving cars. Methods: The proposed technique is mainly a pipeline of computer vision algorithms that augment each other and take in raw RGB images to produce the required lane-line segments that represent the boundary of the road for the car. The main emphasis of the proposed technique in on simplicity and fast computation capability so that it can be embedded in affordable CPUs that are employed by ADAS systems. Results: Each used algorithm is described in details, implemented and its performance is evaluated using actual road images and videos captured by the front mounted camera of the car. The whole pipeline performance is also tested and evaluated on real videos. Conclusion: The evaluation of the proposed technique shows that it reliably detects and tracks road boundaries under various conditions.
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Huang, Qiao, and Jinlong Liu. "Practical limitations of lane detection algorithm based on Hough transform in challenging scenarios." International Journal of Advanced Robotic Systems 18, no. 2 (March 1, 2021): 172988142110087. http://dx.doi.org/10.1177/17298814211008752.

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The vision-based road lane detection technique plays a key role in driver assistance system. While existing lane recognition algorithms demonstrated over 90% detection rate, the validation test was usually conducted on limited scenarios. Significant gaps still exist when applied in real-life autonomous driving. The goal of this article was to identify these gaps and to suggest research directions that can bridge them. The straight lane detection algorithm based on linear Hough transform (HT) was used in this study as an example to evaluate the possible perception issues under challenging scenarios, including various road types, different weather conditions and shades, changed lighting conditions, and so on. The study found that the HT-based algorithm presented an acceptable detection rate in simple backgrounds, such as driving on a highway or conditions showing distinguishable contrast between lane boundaries and their surroundings. However, it failed to recognize road dividing lines under varied lighting conditions. The failure was attributed to the binarization process failing to extract lane features before detections. In addition, the existing HT-based algorithm would be interfered by lane-like interferences, such as guardrails, railways, bikeways, utility poles, pedestrian sidewalks, buildings and so on. Overall, all these findings support the need for further improvements of current road lane detection algorithms to be robust against interference and illumination variations. Moreover, the widely used algorithm has the potential to raise the lane boundary detection rate if an appropriate search range restriction and illumination classification process is added.
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Li, Qingquan, Jian Zhou, Bijun Li, Yuan Guo, and Jinsheng Xiao. "Robust Lane-Detection Method for Low-Speed Environments." Sensors 18, no. 12 (December 4, 2018): 4274. http://dx.doi.org/10.3390/s18124274.

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Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles.
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Kumar H D*, Arun, and Prabhakar C J. "Detection and Tracking of Lane Crossing Vehicles in Traffic Video for Abnormality Analysis." International Journal of Engineering and Advanced Technology 10, no. 4 (April 30, 2021): 1–9. http://dx.doi.org/10.35940/ijeat.c2141.0410421.

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In this paper, we present a novel approach for detection and tracking of lane crossing/illegal lane crossing vehicles in traffic video of urban highways. For that intention, an initial pace is performed that estimates the road region of the geometrical structure. After finding the road region, every vehicle is tracked in order to detect lane crossing vehicles according to the distance between lane lines and vehicle centre, it is followed by tracking of lane crossing vehicles based on model-based strategy. The proposed system has been evaluated using recall and precision metric, which are received using experiments carried on selected video sequences of GRAM-RTM dataset and publically available video sequences. The experimental results present that our method reaches the highest accuracy for detection of vehicles and tracking of lane crossing vehicles.
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Cao, Song, Song, Xiao, and Peng. "Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments." Sensors 19, no. 14 (July 18, 2019): 3166. http://dx.doi.org/10.3390/s19143166.

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Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles.
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Fan, Chao, Li Long Hou, Shuai Di, and Jing Bo Xu. "Research on the Lane Detection Algorithm Based on Zoning Hough Transformation." Advanced Materials Research 490-495 (March 2012): 1862–66. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1862.

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In order to improve the adaptability of the lane detection algorithm under complex conditions such as damaged lane lines, covered shadow, insufficient light, the rainy day etc. Lane detection algorithm based on Zoning Hough Transform is proposed in this paper. The road images are processed by the improved ±45° Sobel operators and the two-dimension Otsu algorithm. To eliminate the interference of ambient noise, highlight the dominant position of the lane, the Zoning Hough Transform is used, which can obtain the parameters and identify the lane accurately. The experiment results show the lane detection method can extract the lane marking parameters accurately even for which are badly broken, and covered by shadow or rainwater completely, and the algorithm has good robustness.
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Liu, Dongfang, Yaqin Wang, Tian Chen, and Eric T. Matson. "Accurate Lane Detection for Self-Driving Cars: An Approach Based on Color Filter Adjustment and K-Means Clustering Filter." International Journal of Semantic Computing 14, no. 01 (March 2020): 153–68. http://dx.doi.org/10.1142/s1793351x20500038.

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Lane detection is a crucial factor for self-driving cars to achieve a fully autonomous mode. Due to its importance, lane detection has drawn wide attention in recent years for autonomous driving. One challenge for accurate lane detection is to deal with noise appearing in the input image, such as object shadows, brake marks, breaking lane lines. To address this challenge, we propose an effective road detection algorithm. We leverage the strength of color filters to find a rough localization of the lane marks and employ a K-means clustering filter to screen out the embedded noises. We use an extensive experiment to verify the effectiveness of our method. The result indicates that our approach is robust to process noises appearing in input image, which improves the accuracy in lane detection.
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Dissertations / Theses on the topic "Detection of road lane lines"

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Borkar, Amol. "Multi-viewpoint lane detection with applications in driver safety systems." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/43752.

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The objective of this dissertation is to develop a Multi-Camera Lane Departure Warning (MCLDW) system and a framework to evaluate it. A Lane Departure Warning (LDW) system is a safety feature that is included in a few luxury automobiles. Using a single camera, it performs the task of informing the driver if a lane change is imminent. The core component of an LDW system is a lane detector, whose objective is to find lane markers on the road. Therefore, we start this dissertation by explaining the requirements of an ideal lane detector, and then present several algorithmic implementations that meet these requirements. After selecting the best implementation, we present the MCLDW methodology. Using a multi-camera setup, MCLDW system combines the detected lane marker information from each camera's view to estimate the immediate distance between the vehicle and the lane marker, and signals a warning if this distance is under a certain threshold. Next, we introduce a procedure to create ground truth and a database of videos which serve as the framework for evaluation. Ground truth is created using an efficient procedure called Time-Slicing that allows the user to quickly annotate the true locations of the lane markers in each frame of the videos. Subsequently, we describe the details of a database of driving videos that has been put together to help establish a benchmark for evaluating existing lane detectors and LDW systems. Finally, we conclude the dissertation by citing the contributions of the research and discussing the avenues for future work.
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Paula, Maurício Braga de. "Visão computacional para veículos inteligentes usando câmeras embarcadas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/122511.

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O uso de sistemas de assistência ao motorista (DAS) baseados em visão tem contribuído consideravelmente na redução de acidentes e consequentemente no auxílio de uma melhor condução. Estes sistemas utilizam basicamente uma câmera de vídeo embarcada (normalmente fixada no para-brisa) com o propósito de extrair informações acerca da rodovia e ajudar o condutor num melhor processo de dirigibilidade. Pequenas distrações ou a perda de concentração podem ser suficientes para que um acidente ocorra. Este trabalho apresenta uma proposta para o desenvolvimento de algoritmos para extrair informações sobre a sinalização em rodovias. Mais precisamente, serão abordados algoritmos de calibração de câmera explorando a geometria da pista, de extração da marcação de pintura (sinalização horizontal) e detecção e identificação de placas de trânsito (sinalização vertical). Os resultados experimentais indicam que o método de calibração de câmera alcançou bons resultados na obtenção dos parâmetros extrínsecos com erros inferiores a 0:5 . O erro médio encontrado nos experimentos com relação a estimativa da altura da câmera foi em torno de 12 cm (erro relativo aproximado de 10%), permitindo explorar o uso da realidade aumentada como uma possível aplicação. A acurácia global para a detecção e reconhecimento da sinalização horizontal (marcas seccionadas, contínuas e mistas) foi acima de 96% perante uma diversidade de situações apresentadas, tais como: sombras, variação de iluminação, degradação do asfalto e pintura. O uso da câmera calibrada para a detecção da sinalização vertical contribui para delimitar o espaço de varredura da janela deslizante do detector, bem como realizar a procura por placas em uma única escala para cada região de busca, caracterizada pela distância ao veículo. Os resultados apresentados reportam uma taxa global de classificação de aproximadamente 99% para o sinal de proibido ultrapassar, considerando-se uma base de dados limitada a 962 amostras.
The use of driver assistance systems (DAS) based on computer vision has helped considerably in reducing accidents and consequently aid in better driving. These systems primarily use an embedded video camera (usually fixed on the windshield) for the purpose of extracting information about the highway and assisting the driver in a better handling process. Small distractions or loss of concentration may be sufficient for an accident to occur. This work presents the development of algorithms to extract information about traffic signs on highways. More specifically, this work will tackle a camera calibration algorithm that exploits the geometry of the road track, algorithms for the extraction of road marking paint (lane markings) and detection and identification of vertical traffic signs. Experimental results indicate that the proposed method for obtaining the extrinsic parameters achieve good results with errors of less than 0:5 . The average error in our experiments, related to the camera height, were around 12 cm (relative error around 10%). Global accuracy for the detection and classification of road lane markings (dashed, solid, dashed-solid, solid-dashed or double solid) were over 96%. Finally, our camera calibration algorithm was used to reduce the search region and to define the scale of a slidingwindow detector for vertical traffic signs. The use of the calibrated camera for the detection of traffic signs contributes to define the scanning area of the sliding window and perform a search for signs on a unique scale for each region of interest, determined by the distance to the vehicle. The results reported a global classification rate of approximately 99% for the no overtaking sign, considering a limited of 962 samples.
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Vigren, Malcolm, and Linus Eriksson. "End-to-End Road Lane Detection and Estimation using Deep Learning." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157645.

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The interest for autonomous driving assistance, and in the end, self-driving cars, has increased vastly over the last decade. Automotive safety continues to be a priority for manufacturers, politicians and people alike. Visual-based systems aiding the drivers have lately been boosted by advances in computer vision and machine learning. In this thesis, we evaluate the concept of an end-to-end machine learning solution for detecting and classifying road lane markings, and compare it to a more classical semantic segmentation solution. The analysis is based on the frame-by-frame scenario, and shows that our proposed end-to-end system has clear advantages when it comes detecting the existence of lanes and producing a consistent, lane-like output, especially in adverse conditions such as weak lane markings. Our proposed method allows the system to predict its own confidence, thereby allowing the system to suppress its own output when it is not deemed safe enough. The thesis finishes with proposed future work needed to achieve optimal performance and create a system ready for deployment in an active safety product.
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FERHATOVIC, SANEL. "Comparative study on road and lane detection inmixed criticality embedded systems." Thesis, KTH, Mekatronik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217523.

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One of the main challenges for advanced driver assistance systems (ADAS)is the environment perception problem. One factor that makes ADAS hardto implement is the large amount of different conditions that have to betaken care of. The main sources for condition diversity are lane and roadappearance, image clarity issues and poor visibility conditions. A review ofcurrent lane detection algorithms has been carried out and based on that alane detection algorithm has been developed and implemented on a mixedcriticality platform. The thesis is part of a larger group project consisting offive master thesis students creating a demonstrator for autonomous platoondriving. The final lane detection algorithms consists of preprocessing stepswhere the image is converted to gray scale and everything except the regionof interest (ROI) is cut away. OpenCV, a library for image processing hasbeen utilized for edge detection and hough transform. An algorithm for errorcalculations is developed which compares the center and direction of the lanewith the actual vehicle position and direction during real experiments. Thelane detection system is implemented on a Raspberry Pi which communicateswith a mixed criticality platform through UART. The demonstrator vehiclecan achieve a measured speed of 3.5 m/s with reliable lane keeping using thedeveloped algorithm. It seems that the bottleneck is the lateral control ofthe vehicle rather than lane detection, further work should focus on controlof the vehicle and possibly extending the ROI to detect curves in an earlierstage.
En stor utmaning för avancerade förarstödsystem (ADAS) är problemet med uppfattning av miljön runt omkring. En faktor som gör ADAS svårt att implementera är den stora mängd olika förhållanden som måste tas hand om. De största källorna till olikheter är utseendet på körfältet och vägen, dåliga siktförhållanden samt otydliga bilder. En granskning av nuvarande algoritmer för körfältsdetektering har utförts och baserat på den har en körfältsdetekteringsalgoritm utvecklats och implementerats på ett blandkritiskt system. Avhandlingen är en del av ett större grupprojekt bestående av fem mastersstudenter som ska skapa en demonstrator för autonom konvojkörning. Den slutgiltiga körfältsdetekteringsalgoritmen består av förbehandlingssteg, där bilden konverteras till gråskala och allt utom intresseområdet är bortklippt. OpenCV, ett bibliotek för bildbehandling har använts för kantdetektering och houghtransformation. En algoritm som jämför körfältets mittpunkt och riktning med fordonets faktiska position och riktning har utvecklats och används i experiment för kontroll av fordonet. Körfältsdetekteringsalgoritmen har implementeras på en Raspberry Pi som kommunicerar med en blandkritisk plattform genom UART. Demo-fordonet kan uppnå en uppmätt hastighet på 3,5 m/s med pålitlig väghållning med den utvecklade algoritmen. Det verkar som att flaskhalsen är kontroll av fordonet i sidled och inte körfältsdetektering, ytterligare arbete bör fokusera på kontroll av fordonet och eventuellt utöka synfältet för att detektera kurvor i ett tidigare skede.
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Chahal, Ashwani. "In Situ Detection of Road Lanes Using Raspberry Pi." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7051.

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A self-driven car is a vehicle that can drive without human intervention by making correct decisions based on the environmental conditions. Since the innovation is in its beginning periods, totally moving beyond the human inclusion is still a long shot. However, rapid technological advancements are being made towards the safety of the driver and the passengers. One such safety feature is a Lane Detection System that empowers vehicle to detect road lane lines in various climate conditions. This research provides a feasible and economical solution to detect the road lane lines while driving in a sunny, rainy, or snowy weather condition. An algorithm is designed to perform real time road lane line detection on a low voltage computer that can be easily powered in a regular auto vehicle. The algorithm runs on a RaspberryPi computer placed inside the car. A camera, attached to the vehicle’s windshield, captures the real time images and passes them to the RaspberryPi for processing. The algorithm processes each frame and determines the lane lines. The detected lane lines can be viewed on a 7 inch display screen connected to the Raspberry Pi. The entire system is mounted inside a Jeep Wrangler to conduct the experiments and is powered by the vehicle’s standard charger of 12V-15V power supply. The algorithm provides approximately 97% accurate detection of road lane lines in all weather conditions.
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McMichael, Scott Thomas. "Lane Detection for DEXTER, an Autonomous Robot, in the Urban Challenge." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1201273995.

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Chen, Yue. "A Novel Lightweight Lane Departure Warning System Based on Computer Vision for Improving Road Safety." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42125.

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With the rapid improvement of the Advanced Driver Assistant System (ADAS), autonomous driving has become one of the most common hot topics in recent years. While driving, many technologies related to autonomous driving choose to use the sensors installed on the vehicle to collect the information of road status and the environment outside. This aims to warn the driver to perceive the potential danger in the fastest time, which has become the focus of autonomous driving in recent years. Although autonomous driving brings plenty of conveniences to people, the safety of it is still facing difficulties. During driving, even the experienced driver can not guarantee focus on the status of the road all the time. Thus, lane departure warning system (LDWS) becomes developed. The purpose of LDWS is to determine whether the vehicle is in the safe driving area. If the vehicle is out of this area, LDWS will detect it and alert the driver by the sensors, such as sound and vibration, in order to make the driver back to the safe driving area. This thesis proposes a novel lightweight LDWS model LEHA, which divides the entire LDWS into three stages: image preprocessing, lane detection, and lane departure recognition. Different from the deep learning methods of LDWS, our LDWS model LEHA can achieve high accuracy and efficiency by relying only on simple hardware. The image preprocessing stage aims to process the original road image to remove the noise which is irrelevant to the detection result. In this stage, we apply a novel algorithm of grayscale preprocessing to convert the road image to a grayscale image, which removes the color of it. Then, we design a binarization method to greatly extract the lane lines from the background. A newly-designed image smoothing is added to this stage to reduce most of the noise, which improves the accuracy of the following lane detection stage. After obtaining the processed image, the lane detection stage is applied to detect and mark the lane lines. We use region of interest (ROI) to remove the irrelevant parts of the road image to reduce the detection time. After that, we introduce the Canny edge detection method, which aims to extract the edges of the lane lines. The last step of LDWS in the lane detection stage is a novel Hough transform method, the purpose of it is to detect the position of the lane and mark it. Finally, the lane departure recognition stage is used to calculate the deviation distance between the vehicle and the centerline of the lane to determine whether the warning needs to turn on. In the last part of this paper, we present the experiment results which show the comparison results of different lane conditions. We do the statistic of the proposed LDWS accuracy in terms of detection and departure. The detection rate of our proposed LDWS is 98.2% and the departure rate of it is 99.1%. The average processing time of our proposed LDWS is 20.01 x 10⁻³s per image.
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Feng, Zhaofei [Verfasser], and W. [Akademischer Betreuer] Wiesbeck. "Lane and Road Marking Detection with a High Resolution Automotive Radar for Automated Driving / Zhaofei Feng ; Betreuer: W. Wiesbeck." Karlsruhe : KIT-Bibliothek, 2019. http://d-nb.info/1194061818/34.

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Zacpal, Michal. "Monitorování dopravní situace s využitím Raspberry PI." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221265.

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This thesis describes the design and subsequent implementation of a unit for traffic monitoring using Raspberry PI. First section provides a quick overview of assistance systems, which use a road lane detection techniques. Next there is a description of two diferent methods for road lane detection. Follow the description of monitoring scene. Then the work describe the practical part including the design and realization of supporting electronics, selecting of each components, including the modifying of cameras, mechanical design and creating of unit. Another section is about selection and installation of appropriate software components necessary for running of the unit and the selection of development tools for creating user application. After description of graphical user interafce, there is a description of road lanes detection algorithm. At the end of the thesis is summarized a reliability of unit in real traffic situation. At the appendix there are technical drawings, describing the unit.
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Chen, Jerome Jianrong, and 陳建榕. "Lane Detection for Channelizing Lines." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/843n23.

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碩士
國立交通大學
電控工程研究所
107
In this thesis we consider one of challenge cases in a lane detection system, which is channelizing line, and propose an efficient method to not only keep the pixels belonging to the lane located on the channelizing line, but also detect and model the lanes which were filtered from the observation. Channelizing lines are used to guide drivers where travel in the same direction for permitting on both sides, such as entrance and exit ramps. Channelizing line usually has same intensity with the lane, but has pavement markings, solid white lines with wide diagonal lines or chevrons within two channelizing lines, which are usually influence the detection result in several previous work. The experiments show that the proposal has a good performance even passing through several channelizing lines. While passing through single channelizing line zone, the average accuracy can reach to 91.11%. Experiments show that our approach reaches competitive performances on channelizing lines.
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Books on the topic "Detection of road lane lines"

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Kopf, Jaime. Reflectivity of pavement markings: Analysis of retroreflectivity degradation curves. [Olympia, Wash.]: Washington State Dept. of Transportation, 2004.

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Deer, Randall. Inlaid traffic lane lines: I-90, Edgewick Road to Hyak : post construction/annual report, Experimental Feature WA 86-13. [Olympia, Wash.?]: Washington State Dept. of Transportation, Planning, Research and Public Transportation Division in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1989.

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Thrush, M. J. Assessing passing opportunities: Literature review. Wellington, N.Z: Transit New Zealand, 1996.

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Toro, Guillermo del, Celso García, Bertha Navarro, and Alejandro Springall. La delgada línea amarilla. 2015.

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Book chapters on the topic "Detection of road lane lines"

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Du, Huan, Zheng Xu, and Yong Ding. "The Fast Lane Detection of Road Using RANSAC Algorithm." In Advances in Intelligent Systems and Computing, 1–7. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67071-3_1.

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Ślot, Krzysztof, Michał Strzelecki, Agnieszka Krawczyńska, and Maciej Polańczyk. "Road Lane Detection with Elimination of High-Curvature Edges." In Computer Vision and Graphics, 33–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02345-3_4.

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Liu, Weirong, and Shutao Li. "An Effective Lane Detection Algorithm for Structured Road in Urban." In Intelligent Science and Intelligent Data Engineering, 759–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36669-7_92.

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Xiao, Jinsheng, Li Luo, Yuan Yao, Wentao Zou, and Reinhard Klette. "Lane Detection Based on Road Module and Extended Kalman Filter." In Image and Video Technology, 382–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75786-5_31.

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Park, Hyunhee. "Robust Road Lane Detection for High Speed Driving of Autonomous Vehicles." In Advances in Intelligent Systems and Computing, 256–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15035-8_24.

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Prusty, Pankaj, and Bibhuprasad Mohanty. "A Framework for Real-Time Lane Detection Using Spatial Modelling of Road Surfaces." In Lecture Notes in Networks and Systems, 135–40. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2774-6_17.

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Li, Tianyi, Ming Yang, Xiaojun Xu, Xiang Zhou, and Chunxiang Wang. "A Lane Change Detection and Filtering Approach for Precise Longitudinal Position of On-Road Vehicles." In Intelligent Autonomous Systems 14, 897–907. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-48036-7_65.

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Jha, Pooja, and K. Sridhar Patnaik. "Self-Driving Cars." In Handbook of Research on Emerging Trends and Applications of Machine Learning, 490–507. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9643-1.ch023.

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Human errors are the main cause of vehicle crashes. Self-driving cars bear the promise to significantly reduce accidents by taking the human factor out of the equation, while in parallel monitor the surroundings, detect and react immediately to potentially dangerous situations and driving behaviors. Artificial intelligence tool trains the computers to do things like detect lane lines and identify cyclists by showing them millions of examples of the subject at hand. The chapter in this book discusses the technological advancement in transportation. It also covers the autonomy used according to The National Highway Traffic Safety Administration (NHTSA). The functional architecture of self-driving cars is further discussed. The chapter also talks about two algorithms for detection of lanes as well as detection of vehicles on the road for self-driving cars. Next, the ethical discussions surrounding the autonomous vehicle involving stakeholders, technologies, social environments, and costs vs. quality have been discussed.
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Townsend, Sylvia. "The Fast Lane: Sex, Drugs, and Rock ’n’ Roll Stars." In Bumpy Road, 84–95. University Press of Mississippi, 2019. http://dx.doi.org/10.14325/mississippi/9781496804143.003.0005.

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In this chapter, the film company arrives in Tucumcari, New Mexico to shoot the gas station scene where the race is set up – a tour de force of cinematography. Jaclyn Hellman blows up at her husband, Monte, over his affair with the leading lady, 17-year-old Laurie Bird. Some of the cast and filmmakers indulge in drugs, but they don’t hinder the filmmaking much except for Dennis Wilson, who is stoned nearly all the time and can’t remember his lines. But he and the amateur, off-balance Laurie Bird cause Hellman to call for 10, 15 and even 25 takes when they blow their lines. Joni Mitchell visits her boyfriend, James Taylor, and the actor Harry Dean Stanton arrives for his scene, in which he plays a gay cowboy hitchhiker.
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Xing, Yang, Chen Lv, and Dongpu Cao. "Design of Integrated Road Perception and Lane Detection System for Driver Intention Inference." In Advanced Driver Intention Inference, 77–98. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-819113-2.00004-x.

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Conference papers on the topic "Detection of road lane lines"

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Rakotondrajao, Fabien, and Kharittha Jangsamsi. "Road Boundary Detection for Straight and Curved Lane Lines." In AICCC 2019: 2019 2nd Artificial Intelligence and Cloud Computing Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3375959.3375989.

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Farag, Wael, and Zakaria Saleh. "Road Lane-Lines Detection in Real-Time for Advanced Driving Assistance Systems." In 2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). IEEE, 2018. http://dx.doi.org/10.1109/3ict.2018.8855797.

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Rakotondrajao, Fabien, and Kharittha Jangsamsi. "Road Boundary Detection for Straight Lane Lines Using Automatic Inverse Perspective Mapping." In 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2019. http://dx.doi.org/10.1109/ispacs48206.2019.8986330.

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Wang, Jiannan, Hongbin Ma, Xinghong Zhang, and Xiaomeng Liu. "Detection of Lane Lines on Both Sides of Road Based on Monocular Camera." In 2018 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2018. http://dx.doi.org/10.1109/icma.2018.8484630.

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Liu, Shiwang, Linhong Lu, Xunyu Zhong, and Jianping Zeng. "Effective Road Lane Detection and Tracking Method Using Line Segment Detector." In 2018 37th Chinese Control Conference (CCC). IEEE, 2018. http://dx.doi.org/10.23919/chicc.2018.8482552.

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Wang, Zaiying, Ying Fan, and Hao Zhang. "Lane-line Detection Algorithm for Complex Road Based on OpenCV." In 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2019. http://dx.doi.org/10.1109/imcec46724.2019.8983919.

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Yasui, Nobuhiko, Atsushi Iisaka, and Noboru Nomura. "White Road Line Recognition Using Lane Region Extraction and Line Edge Detection." In International Congress & Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1998. http://dx.doi.org/10.4271/981167.

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Li, Yongfu, and Zhanji Yang. "Progressive Probabilistic Hough Transform Based Nighttime Lane Line Detection for Micro-Traffic Road." In 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2018. http://dx.doi.org/10.1109/cyber.2018.8688188.

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Sultana, Samia, and Boshir Ahmed. "Robust Nighttime Road Lane Line Detection using Bilateral Filter and SAGC under Challenging Conditions." In 2021 IEEE 13th International Conference on Computer Research and Development (ICCRD). IEEE, 2021. http://dx.doi.org/10.1109/iccrd51685.2021.9386516.

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Tan, Yin, and Bassem Hassan. "A Concept of Camera Test-Bench for Testing Camera Based Advanced Driver Assistance Systems." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12996.

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About 1.5 million traffic accidents occur every year in European traffic; most of them are results of careless driving behavior, for example unintentional lane departure. Studies show that these accidents could often be avoided, if the driver would react only half a second earlier. Therefore, advanced driver assistance systems (ADAS) and especially the camera based ADAS increasingly gain importance in the automobile industry because of their diverse functions, for example the lane keeping assistance. Their development must contain an excessive and accurate testing process, in order to contribute to a reliable result in the common use. The importance of the testing methods and environments is growing due to several reasons, for example the need for more flexibility during testing and cost-reduction at the same time. Those requirements can be fully fulfilled by the use of virtual environments. To simplify the testing process, the virtual environment does not need to be as realistic as possible. It only has to provide necessary features for the camera system. Due to different detection algorithms for diverse assistance functions, the virtual environment needs determined characteristics, which are crucial for further precise analyses. This paper presents a concept to create a camera test-bench for testing of camera based ADAS, by adapting an existing virtual environment for the camera system, identifying and enhancing the important features for the testing detection function while enable a flexible and low-cost testing environment. The testing ADAS shall record the virtual driving scenes and deliver the same results as in real environments. First, possible existing approaches of testing processes shall be presented. In this paper, the concept is to shown by the example of road detection. The focus lies on the lane markings and the ground. While the separation lines between diverse objects are clearly visible in the virtual environment, the real ones often differ due to road condition or lighting intensity. The differences must be considered and corrected by creating a shader which adjusts the scenes, before the virtual representations can be applied for testing camera based ADAS. Furthermore, for testing other functions, the shader shall be expanded and contain more relevant feature parameters for different test functions. All in all, as much as possible camera based ADAS shall be tested with a virtual environment and the tests shall help accelerating the development time and improving the quality of camera based ADAS.
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