DrivAid: Augmenting Driving Analytics with Multi-Modal Information

Qi, Bozhao; Liu, Peng; Ji, Tao; Zhao, Wei; Banerjee, Suman

doi:10.1109/vnc.2018.8628415

Cited by 20 publications

(5 citation statements)

References 12 publications

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“…The literature in computer science and electrical engineering has also considered similar questions, specifically many contributions aim at identifying the context of driving events. In that sense, Qi et al (2018) build a system called DrivAid that is able to detect three types of driving activities, namely, turns, hard brakes, and lane changes. They also extract context information from audio–visual signals in real time to develop a deep understanding of driving events.…”

Section: Near‐miss Telematics and Usage‐based Insurancementioning

confidence: 99%

Near‐miss telematics in motor insurance

Guillén

Nielsen

Pérez-Marín

2021

J of Risk & Insurance

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We present a method to integrate telematics data in a pay‐how‐you‐drive insurance pricing scheme that penalizes some near‐miss events. We illustrate our method with a sample of drivers for whom information on near‐miss events and claims frequency records are available. We discuss the implications for motor insurance ratemaking. Our pricing principle is to combine a baseline insurance premium with added extra charges for near‐miss events indicating risky driving (or discounts) that can be updated on a weekly basis. This procedure provides an incentive for safe driving. In our real‐case study illustration, hard‐braking and acceleration events as well as smartphone use while driving increase the cost of insurance.

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Section: Near‐miss Telematics and Usage‐based Insurancementioning

confidence: 99%

Near‐miss telematics in motor insurance

Guillén

Nielsen

Pérez-Marín

2021

J of Risk & Insurance

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“…Othman et al [40] designed a domain adaption network to overcome the issues of a domain shift in classification scenarios where the labeled images from the source domain and unlabeled ones from the target have completely different geographical features. Overall, when it comes to the problems of domain shift between the source and target domains, the DA technique can not only reduce the costs of data preparation, but also improve image recognition [41,42].…”

Section: Transfer Learning and Domain Adaptationmentioning

confidence: 99%

Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

Zhao

Yamada

et al. 2020

Remote Sensing

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In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

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“…Although many investigations into the concept and perspective of edge computing has been carried out [34]- [37], [44], [47], the specific applications that apply this paradigm are rare, the examples including a mobile edge computing-based video streaming technique [38] proposed by Trans et al, and Kumar et al leveraging edge computing to handle large data sets in smart electric grids [39]. Our work, benefitting much from the edge computing architecture, is yet another concrete application contributed to this realm.…”

Section: Edge Computingmentioning

confidence: 99%

Vivid: Augmenting Vision-Based Indoor Navigation System With Edge Computing

Zhao

et al. 2020

IEEE Access

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Indoor localization and navigation have a great potential of application, especially in large indoor spaces where people tend to get lost. The indoor localization problem is the fundamental of an indoor navigation system. Existing research and commercial efforts have leveraged wireless-based approaches to locate users in indoor environments. However, the predominant wireless-based approaches, such as WiFi and Bluetooth, are still not satisfactory, either not supporting commodity devices, or being vulnerable to environmental changes. These issues make them hard to deploy and maintain. In this paper, we present Vivid, a mobile device-friendly indoor localization and navigation system that leverages visual cues as the cornerstone of localization. By leveraging the computation power at the extreme internet edges, Vivid to a large extent overcomes the difficulties brought by resource-intensive image processing tasks. We propose a grid-based algorithm that transforms the feature map into a grid, with which finding the path between two positions can be easily obtained. We also leverage deep learning techniques to assist in automatic map maintenance to adapt to the visual changes and make the system more robust. With edge computing, user privacy is preserved since the visual data is mainly processed locally and detected dynamic objects are removed immediately without saving to databases. The evaluation results show that: i) our system easily outperforms the existing solutions on COTS devices in localization accuracy, yielding decimeter-level error; ii) our choice of the system architecture is scalable and optimal among the available ones; iii) the automatic map maintenance mechanism effectively ameliorates the localization robustness of the system.INDEX TERMS Last mile delivery, loTs-based indoor localization and navigation, edge computing for loTs sensors.

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DrivAid: Augmenting Driving Analytics with Multi-Modal Information

Cited by 20 publications

References 12 publications

Near‐miss telematics in motor insurance

Near‐miss telematics in motor insurance

Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

Vivid: Augmenting Vision-Based Indoor Navigation System With Edge Computing

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