A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Lin, Huei-Yung; Dai, Jyun-Min; Wu, Lu-Ting; Chen, Liqi

doi:10.3390/s20185139

Cited by 41 publications

(26 citation statements)

References 52 publications

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“…Whilst ANPR technology can come in many different packages, they all perform the same basic function which is to provide a highly accurate system of reading a vehicle without human intervention. It is utilized in very diverse applications such as access control, parking management, tolling, user billing, delivery tracking, traffic management, policing and security services, customer services and directions, the red light and lane enforcement, queue length estimation, and many other services [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Figure 1 shows the basic system diagram of a fixed and mobile ANPR technology.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.

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Section: Introductionmentioning

confidence: 99%

“…2 The publicly available datasets may be subject to privacy restrictions. (Source attributions might be required).…”

mentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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“…Furthermore, the analysis is more complex and difficult in neural-network-based methods than hand-crafted-feature-based methods, as a coherent framework for understanding neural-network-based architectures has remained elusive [ 16 ]. Therefore, lane detection methods using hand-crafted features have still been actively studied [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Graph Model-Based Lane-Marking Feature Extraction for Lane Detection

Yoo

Kim

2021

Sensors

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This paper presents a robust, efficient lane-marking feature extraction method using a graph model-based approach. To extract the features, the proposed hat filter with adaptive sizes is first applied to each row of an input image and local maximum values are extracted from the filter response. The features with the maximum values are fed as nodes to a connected graph structure, and the edges of the graph are constructed using the proposed neighbor searching method. Nodes related to lane-markings are then selected by finding a connected subgraph in the graph. The selected nodes are fitted to line segments as the proposed features of lane-markings. The experimental results show that the proposed method not only yields at least 2.2% better performance compared to the existing methods on the KIST dataset, which includes various types of sensing noise caused by environmental changes, but also improves at least 1.4% better than the previous methods on the Caltech dataset which has been widely used for the comparison of lane marking detection. Furthermore, the proposed lane marking detection runs with an average of 3.3 ms, which is fast enough for real-time applications.

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“…Cameras are widely used for object recognition, while radar and LiDAR are commonly used for distance measurement. LiDAR and radar have the advantage of long-distance measurements of objects in various conditions such as cloudy, rain, day, and night [5]. However, this capability is not sufficient to perform environmental analysis.…”

Section: Introductionmentioning

confidence: 99%

“…LiDAR or radar are often combined with cameras to perform environmental perception recognition. In driver assistance systems, cameras are widely used for traffic sign recognition [6], real-time detection and tracking of pedestrians [7], vehicle detection [8], or forward collision and overtaking detection [5].…”

Section: Introductionmentioning

confidence: 99%

Implementation 2D Lidar and Camera for detection object and distance based on RoS

Mulyanto

Borman

Prasetyawan

et al. 2020

JOIV : Int. J. Inform. Visualization

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The advanced driver assistance systems (ADAS) are one of the issues to protecting people from vehicle collision. Collision warning system is a very important part of ADAS to protect people from the dangers of accidents caused by fatigue, drowsiness and other human errors. Multi-sensors has been widely used in ADAS for environment perception such as cameras, radar, and light detection and ranging (LiDAR). We propose the relative orientation and translation between the two sensors are things that must be considered in performing fusion. we discuss the real-time collision warning system using 2D LiDAR and Camera sensors for environment perception and estimate the distance (depth) and angle of obstacles. In this paper, we propose a fusion of two sensors that is camera and 2D LiDAR to get the distance and angle of an obstacle in front of the vehicle that implemented on Nvidia Jetson Nano using Robot Operating System (ROS). Hence, a calibration process between the camera and 2D LiDAR is required which will be presented in session III. After that, the integration and testing will be carried out using static and dynamic scenarios in the relevant environment. For fusion, we use the implementation of the conversion from degree to coordinate. Based on the experiment, we result obtained an average of 0.197 meters

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A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Cited by 41 publications

References 52 publications

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Graph Model-Based Lane-Marking Feature Extraction for Lane Detection

Implementation 2D Lidar and Camera for detection object and distance based on RoS

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