Driver Danger-Level Monitoring System Using Multi-Sourced Big Driving Data

Yin, Jia-Li; Chen, Bo‐Hao; Lai, K. Robert

doi:10.1109/tits.2019.2954183

Cited by 16 publications

(9 citation statements)

References 36 publications

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“…Furthermore, most recent studies support the idea that drivers still need to focus on the road to reduce the risk of accidents [2] and that in-vehicle infotainment systems are distracting [3]. Utilizing its full capabilities, including sensing capabilities achieved by employing multisensor systems, data acquisition for danger level detection [9]- [11], artificial intelligence, and vehicle-to-everything technology, a vehicle becomes a mobile platform that senses more and also visually warns preemptively even before the driver notices. By detecting potential hazards on time, augmented peripheral cues can help increase driver awareness, thus improving safety.…”

Section: Toward Immersive Hmismentioning

confidence: 99%

Prospective Immersive Human-Machine Interface for Future Vehicles: Multiple Zones Turn the Full Windscreen Into a Head-Up Display

Yöntem

Chu

et al. 2021

IEEE Veh. Technol. Mag.

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Section: Toward Immersive Hmismentioning

confidence: 99%

Prospective Immersive Human-Machine Interface for Future Vehicles: Multiple Zones Turn the Full Windscreen Into a Head-Up Display

Yöntem

Chu

et al. 2021

IEEE Veh. Technol. Mag.

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“…Still, within the context of solutions exploiting CNNs, Bayesian Filters [29] have been used to distinguish between static, mobile, and dynamic objects. Siamese neural networks [30] and also methods without explicit use of CNNs [31] have been proposed to detect various types of features from the environment. Related to our approach, the authors in [32] perform cluster detection based on a cost function with different features, to learn online how to detect humans.…”

Section: Object Detection and Semantic Mappingmentioning

confidence: 99%

Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments

et al. 2020

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The advancements in the robotic field have made it possible for service robots to increasingly become part of everyday indoor scenarios. Their ability to operate and reach defined goals depends on the perception and understanding of their surrounding environment. Detecting and positioning objects as well as people in an accurate semantic map are, therefore, essential tasks that a robot needs to carry out. In this work, we walk an alternative path to build semantic maps of indoor scenarios. Instead of relying on high-density sensory input, like the one provided by an RGB-D camera, and resource-intensive processing algorithms, like the ones based on deep learning, we investigate the use of low-density point-clouds provided by 3D LiDARs together with a set of practical segmentation methods for the detection of objects. By focusing on the physical structure of the objects of interest, it is possible to remove complex training phases and exploit sensors with lower resolution but wider Field of View (FoV). Our evaluation shows that our approach can achieve comparable (if not better) performance in object labeling and positioning with a significant decrease in processing time than established approaches based on deep learning methods. As a side-effect of using low-density point-clouds, we also better support people privacy as the lower resolution inherently prevents the use of techniques like face recognition.

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“…In addition, the alarm system can also be used in conjunction with other vehicle driving systems. For example, in the research of Yin et al, 10 a danger-level framework and its feature extraction method are proposed to analyze driving data. The tire loosening warning signal in this article can be used as one of the high-level hazard features.…”

Section: The Application Of This Alarm Systemmentioning

confidence: 99%

Development of an early warning system for loose automobile tires

Song

et al. 2020

Science Progress

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The looseness of tires or even falling off from cars will lead to serious traffic accidents. Once it occurs, it will bring casualties and huge economic losses to society, seriously affecting the traffic safety. To mitigate such possible safety concerns, an early loosening warning system is developed in this article. The system consists of the tire monitoring module and the working control module. The tire monitoring module is installed on the tire and is designed with no power supply. The control module is installed in the vehicle body. Signal transmission between the two modules is achieved through wireless radio frequency. In the driving, once the tire is loosened, the monitoring device will send out the alarm signal automatically and wirelessly. After the driver gets the alarm signal, he can immediately perform the emergency processing, parking, and inspection, which can avoid traffic accidents caused by it. This article introduces the detailed structure, working principle, and operation process of the system. This early warning system has simple structure, high reliability, and is easy to use. It can be used in the common working environment of automobiles. Meanwhile, it is also the foundation of intelligent connected vehicle.

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Driver Danger-Level Monitoring System Using Multi-Sourced Big Driving Data

Cited by 16 publications

References 36 publications

Prospective Immersive Human-Machine Interface for Future Vehicles: Multiple Zones Turn the Full Windscreen Into a Head-Up Display

Prospective Immersive Human-Machine Interface for Future Vehicles: Multiple Zones Turn the Full Windscreen Into a Head-Up Display

Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments

Development of an early warning system for loose automobile tires

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