An Integrated Framework for Autonomous Driving: Object Detection, Lane Detection, and Free Space Detection

Kemsaram, Narsimlu; Das, Anweshan; Dubbelman, Gijs

doi:10.1109/worlds4.2019.8904020

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…Meanwhile, the Embedding Decoder and Binary Decoder architecture are similar, except for the number of output dimensions. The suggested lane detection system in [77] is based on the Drive Works LaneNet pipeline, which uses camera images. This paper presents an integrated framework for autonomous driving based on the NVidia deep neural network multi-class object identification framework, the lane detection framework, and the free space detection framework.…”

Section: I) Conventional Deep Learningmentioning

confidence: 99%

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Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

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One of the essential systems in autonomous vehicles for ensuring a secure circumstance for drivers and passengers is the Advanced Driver Assistance System (ADAS). Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection are examples of ADAS. Lane detection displays information specific to the geometrical features of lane line structures to the vehicle's intelligent system to show the position of lane markings. This article reviews the methods employed for lane detection in an autonomous vehicle. A systematic literature review (SLR) has been carried out to analyze the most delicate approach to detecting the road lane for the benefit of the automation industry. One hundred and two publications from well-known databases were chosen for this review. The trend was discovered after thoroughly examining the selected articles on the method implemented for detecting the road lane from 2018 until 2021. The selected literature used various methods, with the input dataset being one of two types: self-collected or acquired from an online public dataset. In the meantime, the methodologies include geometric modeling and traditional methods, while AI includes deep learning and machine learning. The use of deep learning has been increasingly researched throughout the last four years. Some studies used stand-alone deep learning implementations for lane detection problems. Meanwhile, some research focuses on merging deep learning with other machine learning techniques and classical methodologies. Recent advancements imply that attention mechanism has become a popular combined strategy with deep learning methods. The use of deep algorithms in conjunction with other techniques showed promising outcomes. This research aims to provide a complete overview of the literature on lane detection methods, highlighting which approaches are currently being researched and the performance of existing state-of-the-art techniques. Also, the paper covered the equipment used to collect the dataset for the training process and the dataset used for network training, validation, and testing. This review yields a valuable foundation on lane detection techniques, challenges, and opportunities and supports new research works in this automation field. For further study, it is suggested to put more effort into accuracy improvement, increased speed performance, and more challenging work on various extreme conditions in detecting the road lane.

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Section: I) Conventional Deep Learningmentioning

confidence: 99%

“…The camera's position should be fixed and usually expected to be in the vehicle's center. Next, a Toyota Prius autonomous driving research prototype vehicle with Nvidia Drive PX 2 and a Sekonix GMSL Camera was used by Kemsaram and Das [77]. In a car, A GMSL connector connects a Sekonix GMSL Camera to a Drive PX 2.…”

Section: ) Cameramentioning

confidence: 99%

Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

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“…Except for object detection, many autonomous driving perception tasks can be formulated as semantic segmentation. For example, free-space detection [35,57,107] is a basic module for many autonomous driving systems which classify the ground pixel into the drivable and non-drivable parts. Some Lane Detection [24,84] methods also use the multi-class semantic segmentation mask to represent the different lanes on the road.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Multi-modal Sensor Fusion for Auto Driving Perception: A Survey

Huang¹,

Shi²,

Li³

et al. 2022

Preprint

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Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers. However, achieving a rather good performance is not an easy task due to the noisy raw data, underutilized information, and the misalignment of multi-modal sensors. In this paper, we provide a literature review of the existing multi-modal-based methods for perception tasks in autonomous driving. Generally, we make a detailed analysis including over 50 papers leveraging perception sensors including LiDAR and camera trying to solve object detection and semantic segmentation tasks. Different from traditional fusion methodology for categorizing fusion models, we propose an innovative way that divides them into two major classes, four minor classes by a more reasonable taxonomy in the view of the fusion stage. Moreover, we dive deep into the current fusion methods, focusing on the remaining problems and open-up discussions on the potential research opportunities. In conclusion, what we expect to do in this paper is to present a new taxonomy of multi-modal fusion methods for the autonomous driving perception tasks and provoke thoughts of the fusion-based techniques in the future.

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“…Labeling the images and then training the features for that class within the tags in the image is essential for model training. For example, there are studies such as following the ball or players in sports competitions [30] and lane tracking in unmanned vehicles [31]. In this study, SSD ResNet50 model, which is one of the mobile object detection models, was used.…”

Section: Object Detectionmentioning

confidence: 99%

Monoküler Derinlik Tahmini ve Yakın Nesnelerin Tespiti

SARIZEYBEK

Işık

2022

Uluslararası Teknolojik Bilimler Dergisi

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The image obtained from the cameras is 2D, so we cannot know how far the object is on the image. In order to detect objects only at a certain distance in a camera system, we need to convert the 2D image into 3D. Depth estimation is used to estimate distances to objects. It is the perception of the 2D image as 3D. Although different methods are used to implement this, the method to be applied in this experiment is to detect depth perception with a single camera. After obtaining the depth map, the obtained image will be filtered by objects in the near distance, the distant image will be closed, a new image will be run with the object detection model and object detection will be performed. The desired result in this experiment is, for projects with a low budget, instead of using dual camera or LIDAR methods, it is to ensure that a robot can detect obstacles that will come in front of it with only one camera. As a result, 8 FPS was obtained by running two models on the embedded device, and the loss value was obtained as 0.342 in the inference test performed on the new image, where only close objects were taken after the depth estimation.

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An Integrated Framework for Autonomous Driving: Object Detection, Lane Detection, and Free Space Detection

Cited by 13 publications

References 13 publications

Lane Detection in Autonomous Vehicles: A Systematic Review

Lane Detection in Autonomous Vehicles: A Systematic Review

Multi-modal Sensor Fusion for Auto Driving Perception: A Survey

Monoküler Derinlik Tahmini ve Yakın Nesnelerin Tespiti

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