Vulnerable objects detection for autonomous driving: A review

Khatab, Esraa; Onsy, Ahmed; Varley, M.R.; Abouelfarag, Ahmed

doi:10.1016/j.vlsi.2021.01.002

Cited by 40 publications

(22 citation statements)

References 41 publications

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“…Of particular interest to the current study is how these sensors perform in relation to VRU detection. The authors of [38] recently conducted a review on this subject. They found that although a comprehensive suite of sensors would achieve high levels of detection, they can still be limited by the algorithms which interpret the data from the sensors, as well as a lack of processing power to process these data quickly enough.…”

Section: Discussionmentioning

confidence: 99%

Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

Morris

Haworth

Filtness

et al. 2021

Behavioral Sciences

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(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

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Section: Discussionmentioning

confidence: 99%

Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

Morris

Haworth

Filtness

et al. 2021

Behavioral Sciences

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“…Many survey papers have discussed different AVs' sensory technologies [5][6][7][8][11][12][13][14][15][16][17][18][19]. In this paper, the proposed approach is based on performing sensor fusion between a 2D LiDAR and a monocular camera to achieve real-time object detection, classification, and 3D localization of objects in complex scenarios where objects are interacting and overlapping.…”

Section: Autonomous Vehicles' Sensory Systemsmentioning

confidence: 99%

“…In addition to the advantages of AVs, there are some challenges facing their widespread use, such as: legal terms, cybersecurity, traffic management strategies, moral and ethical challenges, and operational challenges [5,6].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles

Khatab

Onsy

Abouelfarag

2022

Sensors

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One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due to their continuously changing behavior. The majority of commercially available AVs, and research into them, depends on employing expensive sensors. However, this hinders the development of further research on the operations of AVs. In this paper, therefore, we focus on the use of a lower-cost single-beam LiDAR in addition to a monocular camera to achieve multiple 3D vulnerable object detection in real driving scenarios, all the while maintaining real-time performance. This research also addresses the problems faced during object detection, such as the complex interaction between objects where occlusion and truncation occur, and the dynamic changes in the perspective and scale of bounding boxes. The video-processing module works upon a deep-learning detector (YOLOv3), while the LiDAR measurements are pre-processed and grouped into clusters. The output of the proposed system is objects classification and localization by having bounding boxes accompanied by a third depth dimension acquired by the LiDAR. Real-time tests show that the system can efficiently detect the 3D location of vulnerable objects in real-time scenarios.

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“…Real time leak detectetion for automated hydro carbon in industry was proposed with faster R-CNN and the model get compared with SSD [20]. Detection of vulnearable objects in autonomous driving of vehicle with DNN [21]. VGG16 based faster R-CNN was proposed for the detection of vehicles in complex traffic [22].…”

Section: Introductionmentioning

confidence: 99%

Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Karthikeyan

Subashini

2021

IJECE

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Mechanical fasteners are widely used in manufacturing of hardware and mechanical components such as automobiles, turbine & power generation and industries. Object detection method play a vital role to make a smart system for the society. Internet of things (IoT) leads to automation based on sensors and actuators not enough to build the systems due to limitations of sensors. Computer vision is the one which makes IoT too much smarter using deep learning techniques. Object detection is used to detect, recognize and localize the object in an image or a real time video. In industry revolution, robot arm is used to fit the fasteners to the automobile components. This system will helps the robot to detect the object of fasteners such as screw and nails accordingly to fit to the vehicle moved in the assembly line. Faster R-CNN deep learning algorithm is used to train the custom dataset and object detection is used to detect the fasteners. Region based convolutional neural networks (Faster R-CNN) uses a region proposed network (RPN) network to train the model efficiently and also with the help of Region of Interest able to localize the screw and nails objects with a mean average precision of 0.72 percent leads to accuracy of 95 percent object detection

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Vulnerable objects detection for autonomous driving: A review

Cited by 40 publications

References 41 publications

Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles

Automated object detection of mechanical fasteners using faster region based convolutional neural networks

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