CarSpeak

Kumar, Swarun; Shi, Lixin; Ahmed, Naveed; Gil, Stephanie; Katabi, Dina; Rus, Daniela

doi:10.1145/2377677.2377724

Cited by 67 publications

(6 citation statements)

References 28 publications

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“…Another aspect is the sensor size: smaller sensors are preferred on the vehicle because of limited space and aerodynamic restraints, and LiDAR is generally larger than radar, stereo camera [5], flash camera [6], event camera [7], and thermal camera [8,9]. However, researchers work on reducing the cost, size, and weight of LiDAR recently [10], but it still needs more work. However, in this paper, we are working on the enhancement of visual data by using the camera.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Haris

Głowacz

2021

Electronics

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Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-time deployment restrictions. We conclude that the YOLOv4 outperforms accurately in detecting difficult road target objects under complex road scenarios and weather conditions in an identical testing environment.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Haris

Głowacz

2021

Electronics

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“…But computer-driven vehicles can be connected -the chances that they might miscommunicate and collide are therefore much smaller. Similarly, it is easier to ensure that automated vehicles comply with rules such as speed limits (not to mention the ban on drink driving), even as those rules change [5][6][7] .…”

Section: The Future Of Workmentioning

confidence: 99%

Reboot for the AI revolution

Harari¹

2017

Nature

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“…The benefits of cooperative perception have been studied in recent years [30][31][32][33]. Motivated by its potential, many researches focused on several areas, including communication protocol [34], generation rules of CPMs [1], and algorithm design. In CarSpeak [34], a content-centric communication protocol around the needs of cooperative perception has been proposed.…”

Section: Current Cooperative Perception Schemesmentioning

confidence: 99%

“…Motivated by its potential, many researches focused on several areas, including communication protocol [34], generation rules of CPMs [1], and algorithm design. In CarSpeak [34], a content-centric communication protocol around the needs of cooperative perception has been proposed. It focuses on sensor information sharing at the medium access control (MAC) layer and is fully integrated with autonomous driving.…”

Section: Current Cooperative Perception Schemesmentioning

confidence: 99%

Pillar-Based Cooperative Perception from Point Clouds for 6G-Enabled Cooperative Autonomous Vehicles

Wang

Guo

Wang

et al. 2022

Wireless Communications and Mobile Computing

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3D object detection is a significant aspect of the perception module in autonomous driving; however, with current technology, data sharing between vehicles and cloud servers for cooperative 3D object detection under the strict latency requirement is limited by the communication bandwidth. The sixth-generation (6G) networks have accelerated the transmission rate of the sensor data significantly with extreme low-latency and high-speed data transmission. However, which sensor data format and when to transmit it are still challenging. To address these issues, this study proposes a cooperative perception framework combined with a pillar-based encoder and Octomap-based compression at edges for connected autonomous vehicles to reduce the amount of missing detection in blind spots and further distances. This approach satisfies the constraints on the accuracy of the task perception and provides drivers or autonomous vehicles with sufficient reaction time by applying fixed encoders to learn a representation of point clouds (LiDAR sensor data). Extensive experiment results show that the proposed approach outperforms the previous cooperative perception schemes running at 30 Hz, and the accuracy of the object bounding box results in further distances (greater than 12 m). Furthermore, this approach achieves a lower total delay for the procession of the fusion data and the transmission of the cooperative perception message. To the best of our knowledge, this study is the first to introduce a pillar-based encoder and Octomap-based compression framework for cooperative perception between vehicles and edges in connected autonomous driving.

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CarSpeak

Cited by 67 publications

References 28 publications

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Reboot for the AI revolution

Pillar-Based Cooperative Perception from Point Clouds for 6G-Enabled Cooperative Autonomous Vehicles

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