Autonomous convoy driving by night: The vehicle tracking system

Fries, Carsten; Wuensche, Hans-Joachim

doi:10.1109/tepra.2015.7219675

Cited by 11 publications

(3 citation statements)

References 20 publications

(29 reference statements)

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“…Weather conditions such as fog or snow have a negative impact on the performance of LiDAR. 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.…”

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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“…There are promising studies [83] to improve monocular camera based depth perception, but modalities that are not negatively affected by illumination and weather conditions are still necessary besides cameras for dynamic driving tasks. Other camera types gaining interest for ADS include flash cameras [78], thermal cameras [80], [81], and event cameras [79].…”

Section: B Sensors and Hardwarementioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS

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“…Multi-vehicle convoys are mostly used in military when personnel and resources need to be transported to different locations, and traveling in a convoy provides extra protection for both vehicles and personnel in adverse environments. In addition to military convoys [16], [17], other sectors include commercial vehicle convoys [18], [19] as well as public urban transport convoys [20].…”

Section: Autonomous Convoy Techniquesmentioning

confidence: 99%

Autonomous Convoying: A Survey on Current Research and Development

et al. 2022

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Convoying or platooning with a fleet of autonomous vehicles, which is denoted as autonomous convoying in this paper, has attracted increasing attention from the research communities, governments, and private sectors in recent years. Autonomous convoying offers immense opportunities due to its potential in enhancing logistical efficiency as well as reducing road incidents/accidents by eliminating human errors due to stress and fatigue. While humans can make complex decisions, involving humans in decision-making processes often causes delays as compared with those of automated machines. Indeed, human errors cause approximately 90% of road accidents and fatalities. Efficient platooning techniques can also reduce fuel consumption and carbon footprints. This paper presents a concise survey on current research and development initiatives in autonomous convoying while critically discussing the underlying techniques and technologies developed in this domain. Implications of autonomous convoying toward different industries are also analyzed and discussed.

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Autonomous convoy driving by night: The vehicle tracking system

Cited by 11 publications

References 20 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

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Autonomous Convoying: A Survey on Current Research and Development

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