Integrating State-of-the-Art CNNs for Multi-Sensor 3D Vehicle Detection in Real Autonomous Driving Environments

Barea, Rafael; Bergasa, Luis M.; Romera, Eduardo; López-Guillén, Elena; Pérez, Olga Gloria Barbón; Tradacete, Miguel; López, Joaquín

doi:10.1109/itsc.2019.8916973

Cited by 8 publications

(12 citation statements)

References 16 publications

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“…Barea et al presented an integrated CNN for multisensory 3D vehicle detection in a real autonomous driving environment [58]. As the title expresses, the proposal of their paper aims to present an outstanding architecture based on the combination of state of the art methods for object detection such as YOLO and Mask R-CNN for 3D segmentation besides a LiDAR point cloud.…”

Section: Image Pre-processing and Instance Segmentationmentioning

confidence: 99%

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

et al. 2021

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Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

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Section: Image Pre-processing and Instance Segmentationmentioning

confidence: 99%

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

et al. 2021

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“…Barea et al presented an integrated state of art CNNs for multisensory 3D vehicle detection in a real autonomous driving environment [60]. As the title expresses, the proposal of their paper aims to present an outstanding architecture based on the combination of state of the art methods for object detection such as YOLO and Mask R-CNN for 3D segmentation besides of a LiDAR point cloud.…”

Section: Image Pre-processing and Instance Segmentationmentioning

confidence: 99%

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Medina¹,

Espitia²,

Silva³

et al. 2021

Preprint

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Autonomous driving systems are increasingly becoming a necessary trend towards building smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that effectively considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper has reviewed various techniques towards proposing our own end-to-end autonomous vehicle system, considering the latest state on the art on computer vision, DSs, path planning, and parallelization.

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“…For a more detailed explanation of the method, we refer the reader to the following reference of the authors [37].…”

Section: Perception Modulementioning

confidence: 99%

“…The outputs of this module are the linear speed and curvature that are sent to the Drive-By-Wire Module. For a more detailed explanation of the method, the reader is referred to [37].…”

Section: Control Modulementioning

confidence: 99%

Drive-By-Wire Development Process Based on ROS for an Autonomous Electric Vehicle

Arango

Bergasa

Revenga

et al. 2020

Sensors

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This paper presents the development process of a robust and ROS-based Drive-By-Wire system designed for an autonomous electric vehicle from scratch over an open source chassis. A revision of the vehicle characteristics and the different modules of our navigation architecture is carried out to put in context our Drive-by-Wire system. The system is composed of a Steer-By-Wire module and a Throttle-By-Wire module that allow driving the vehicle by using some commands of lineal speed and curvature, which are sent through a local network from the control unit of the vehicle. Additionally, a Manual/Automatic switching system has been implemented, which allows the driver to activate the autonomous driving and safely taking control of the vehicle at any time. Finally, some validation tests were performed for our Drive-By-Wire system, as a part of our whole autonomous navigation architecture, showing the good working of our proposal. The results prove that the Drive-By-Wire system has the behaviour and necessary requirements to automate an electric vehicle. In addition, after 812 h of testing, it was proven that it is a robust Drive-By-Wire system, with high reliability. The developed system is the basis for the validation and implementation of new autonomous navigation techniques developed within the group in a real vehicle.

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Integrating State-of-the-Art CNNs for Multi-Sensor 3D Vehicle Detection in Real Autonomous Driving Environments

Cited by 8 publications

References 16 publications

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Drive-By-Wire Development Process Based on ROS for an Autonomous Electric Vehicle

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