A Novel Strategy for Road Lane Detection and Tracking Based on a Vehicle’s Forward Monocular Camera

Andrade, David Carvalho; Bueno, Felipe Augusto Dutra; Franco, Felipe; Silva, Rodrigo Adamshuk; Neme, João Henrique Zander; Margraf, Erick; Omoto, William Takeshi; Farinelli, Felipe Adalberto; Tusset, Ângelo Marcelo; Okida, Sérgio; Santos, Max Mauro Dias; Ventura, Artur; Carvalho, Saulo; Amaral, Rodrigo

doi:10.1109/tits.2018.2856361

Cited by 74 publications

(28 citation statements)

References 31 publications

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“…To establish the mathematical model of a structure, a Hough transform [77] was used in the pre-extraction before curve fitting. A Hough transform and a shape-preserving spline fitted the lane smoothly [78]. In previous study [79], the authors combined a Hough transform and a least-squares line-fitting model, while authors in previous study [80] In order to improve the performance of lane extraction in complex conditions, CNN based on deep learning was implemented.…”

Section: Vision-based Methodsmentioning

confidence: 99%

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Zheng

Yang

et al. 2019

Sustainability

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Autonomous driving is experiencing rapid development. A lane-level map is essential for autonomous driving, and a lane-level road network is a fundamental part of a lane-level map. A large amount of research has been performed on lane-level road network generation based on various on-board systems. However, there is a lack of analysis and summaries with regards to previous work. This paper presents an overview of lane-level road network generation techniques for the lane-level maps of autonomous vehicles with on-board systems, including the representation and generation of lane-level road networks. First, sensors for lane-level road network data collection are discussed. Then, an overview of the lane-level road geometry extraction methods and mathematical modeling of a lane-level road network is presented. The methodologies, advantages, limitations, and summaries of the two parts are analyzed individually. Next, the classic logic formats of a lane-level road network are discussed. Finally, the survey summarizes the results of the review.

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Section: Vision-based Methodsmentioning

confidence: 99%

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Zheng

Yang

et al. 2019

Sustainability

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“…Minimizing MSE can reduce the Euclidean distance between the real data and the fake data generated by generator. The Dice distance, as shown in (3), is commonly used in semantic segmentation, measuring the similarity between two data sets, the prediction results G(x; θ G ) and the real data set y.…”

Section: B Ripple-ganmentioning

confidence: 99%

“…To build a lane line detection system, many challenges The image preprocessing step helps to reduce computational time and improve the performance of algorithms [1]. In this step, researchers may change the color space, for instance from RGB to YCbCr [2], select regions of interest (ROI) [3]- [5], or use some filters such as the median filter [6] and the finite impulse response (FIR) filter [7] to eliminate interference. In the step of feature extraction, brightness-related characteristics, linearity and spectrum are three commonly used features in the lane line detection.…”

Section: Introductionmentioning

confidence: 99%

Ripple-GAN: Lane Line Detection With Ripple Lane Line Detection Network and Wasserstein GAN

Zhang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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With artificial intelligence technology being advanced by leaps and bounds, intelligent driving has attracted a huge amount of attention recently in research and development. In intelligent driving, lane line detection is a fundamental but challenging task particularly under complex road conditions. In this paper, we propose a simple yet appealing network called Ripple Lane Line Detection Network (RiLLD-Net), to exploit quick connections and gradient maps for effective learning of lane line features. RiLLD-Net can handle most common scenes of lane line detection. Then, in order to address challenging scenarios such as occluded or complex lane lines, we propose a more powerful network called Ripple-GAN, by integrating RiLLD-Net, confrontation training of Wasserstein generative adversarial networks, and multi-target semantic segmentation. Experiments show that, especially for complex or obscured lane lines, Ripple-GAN can produce a superior detection performance to other state-of-the-art methods.

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“…Andrade, D.C et al [21] built up a street path location and tracking technique for identifying the paths and tracks the functional requirement for sending the highlights to DAS, which includes LDW and path keeping assist. Here, the information picture is limited for improving the sharpness and the Regions of interest (ROI) is described by utilizing least safe distance with the vehicles.…”

Section: Literature Surveymentioning

confidence: 99%

Multilane Detection Entropy-Based Fusion Model by using Iterative Seed and Optimized Deep Convolutional Neural Network

2019

IJRTE

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In today’s world, the conditions of road is drastically improved as compared with past decade. Most of the express highways are made up of cement concrete and equipped with increased lane size. Apparently speed of the vehicle will increase. Therefore there are more chances for accidents. To avoid the accidents in recent days driver assistance systems are designed to detect the various lane. The detected information of lane path is used for controlling the vehicles and giving alerts to drivers. In this paper the entropy based fusion approach is presents for detecting multi-lanes. The Earth Worm- Crow Search Algorithm (EW-CSA) which is based on Deep Convolution Neural Network(DCNN) is utilized for consolidating the outcomes. At first, the deep learning approaches for path location is prepared using an optimization algorithm and EW-CSA, which focus on characterizing every pixel accurately and require post preparing activities to surmise path data. Correspondingly, the region based segmentation approach is utilizing for the multi-lane detection. An entropy based fusion model is used because this method preserved all the information in the image and reduces the noise effects. The performance of proposed model is analyzed in terms of accuracy, sensitivity, and specificity, providing superior results with values 0.991, 0.992, and 0.887, respectively

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A Novel Strategy for Road Lane Detection and Tracking Based on a Vehicle’s Forward Monocular Camera

Cited by 74 publications

References 31 publications

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Ripple-GAN: Lane Line Detection With Ripple Lane Line Detection Network and Wasserstein GAN

Multilane Detection Entropy-Based Fusion Model by using Iterative Seed and Optimized Deep Convolutional Neural Network

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