Robust Obstacle Detection and Recognition for Driver Assistance Systems

Leng, Jiaxu; Liu, Ying; Du, Dawei; Zhang, Tianlin; Quan, Pei

doi:10.1109/tits.2019.2909275

Cited by 40 publications

(11 citation statements)

References 23 publications

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“…In [ 118 ], a stereo camera was fused with millimeter-wave radar for vehicle detection, where stereo images were acquired to detect nearby vehicles through UV-disparity maps. In [ 119 ], ROIs were extracted based on a UV-disparity map and verified by Faster-RCNN.…”

Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

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Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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“…Identifying this distance is too difficult. For driver assistance system Leng et al [34] proposed a obstacle detection and recognition method. In this system, stereo vision system generates U-V disparity map to detect the obstacles.…”

Section: Driver Drowsiness and Alcohol Detectionmentioning

confidence: 99%

MOVE in ROAD: Multi-Objective Vehicle Monitoring using River Formation Dynamics and Deep Learning Algorithms

Guravaiah

Dharavathu

Udutalapally

et al. 2022

Preprint

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Nowadays, wireless sensor networks plays major role in solving vehicular Internet of Things (VIoT) applications. This paper address the common problems in vehicular technology such as unauthorized vehicular usage within a locality, vehicular thefts, and vehicular accidents using camera, radiofrequency identification (RFID) and Ultra-sonic sensors. It also addresses the problem of identifying the vehicle pollution parameter values such as carbon monoxide ( CO ) and carbon dioxide ( CO 2 ), providing information about alcohol consumption by driver, and checking validity (driving license) of the driver to drive the vehicle. The driver identification is going to be done with the help of driving license, which can be identified with the help of Deep learning algorithms such as MTCNN (Multi-Task Cascaded Convolutional Neural Networks) and facenet algorithms. The proposed system is deployed and demonstrated using Micro-controller, Micro-processor and other sensors in real time environment. The River Formation Dynamics based Multi-hop Routing Protocol for Vehicles (RFDMRPV) is used for communication between vehicles. Data collected from the sensors fixed in vehicles are transferred to server using RFDMRPV for storing and further processing.

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“…The pedestrians are then identified using smart algorithms based on polylines and pattern recognition, as well as dense disparity maps and U-V disparity. In [ 5 ], the authors propose a procedure based on U-V disparity to detect and recognize obstacles on the road as part of a driver assistance system. The proposed realistic U-disparity map greatly improves accuracy when detecting distant obstacles compared to that obtained with a conventional U-disparity map.…”

Section: Introductionmentioning

confidence: 99%

Influence of Pitch Angle Errors in 3D Scene Reconstruction Based on U-V Disparity: A Sensitivity Study

Felipe

Sigut

Sánchez

2021

Sensors

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U-V disparity is a technique that is commonly used to detect obstacles in 3D scenes, modeling them as a set of vertical planes. In this paper, the authors describe the general lines of a method based on this technique for fully reconstructing 3D scenes, and conduct an analytical study of its performance and sensitivity to errors in the pitch angle of the stereoscopic vision system. The equations of the planes calculated for a given error in this angle yield the deviation with respect to the ideal planes (with a zero error in the angle) for a large test set consisting of planes with different orientations, which is represented graphically to analyze the method’s qualitative and quantitative performance. The relationship between the deviation of the planes and the error in the pitch angle is observed to be linear. Two major conclusions are drawn from this study: first, that the deviation between the calculated and ideal planes is always less than or equal to the error considered in the pitch angle; and second, that even though in some cases the deviation of the plane is zero or very small, the probability that a plane of the scene deviates from the ideal by the greatest amount possible, which matches the error in the pitch angle, is very high.

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Robust Obstacle Detection and Recognition for Driver Assistance Systems

Cited by 40 publications

References 23 publications

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

MOVE in ROAD: Multi-Objective Vehicle Monitoring using River Formation Dynamics and Deep Learning Algorithms

Influence of Pitch Angle Errors in 3D Scene Reconstruction Based on U-V Disparity: A Sensitivity Study

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