Deep learning for self-driving cars

Rao, Qing; Frtunikj, Jelena

doi:10.1145/3194085.3194087

Cited by 168 publications

(76 citation statements)

References 5 publications

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“…The RADAR is another field of research within Deep Learning area and could be suggested for future work. 67 For example, the RADAR could be used to obtain micro-Doppler signals, which are distinguished for each object. These micro-Doppler signatures are crucial in training another Convolutional Neural Network, which will allow ensemble models to be built for better resilience and effective results.…”

Section: Resultsmentioning

confidence: 99%

A review of the technological developments for interlocking at level crossing

Fayyaz

Alexoulis-Chrysovergis

Southgate

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part F:

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A Level Crossing remains as one of the highest risk assets within the railway system often depending on the unpredictable behaviour of road and footpath users. For this purpose, interlocking through automated safety systems remains a key area for investigation. Within Europe, 2015–2016, 469 accidents at crossings were recorded of which 288 lead to fatalities and 264 lead to injuries. The European Union’s Agency for Railways has reported that Level Crossing fatalities account for just under 28% of all railway fatalities. This paper identifies suitable obstacle detection technologies and their associated algorithms that can be used to support risk reduction and management of Level Crossings. Furthermore, assessment and decision methods are presented to support their application. Finally state of the art and synergistic opportunity of which a combination of obstacle detection sensors with intelligent decisions layers such as Deep Learning are discussed which can provide robust interlocking decisions for rail applications. The sensor fusion of video camera and RADAR is a promising solution for Level Crossings. By applying additional sensing techniques such as RADAR imaging, further capabilities are added to the system, which can lead to a more robust approach.

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Section: Resultsmentioning

confidence: 99%

A review of the technological developments for interlocking at level crossing

Fayyaz

Alexoulis-Chrysovergis

Southgate

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part F:

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“…The emergence of deep learning has caught much attention and became a presiding technology that introduced a variety of techniques to solve different challenges including self-driving [55], fraud detection [56][57][58], robotics [59], language translations [60], medical diagnosis [61], and many more [62]. Most of these challenges revolve around object detection, classification, segmentation, recognition, and tracking, etc.…”

Section: Monitoring Social Distancing With Deep Learning and A Singlementioning

confidence: 99%

Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera

2021

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The purpose of this work is to provide an effective social distance monitoring solution in low light environments in a pandemic situation. The raging coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has brought a global crisis with its deadly spread all over the world. In the absence of an effective treatment and vaccine the efforts to control this pandemic strictly rely on personal preventive actions, e.g., handwashing, face mask usage, environmental cleaning, and most importantly on social distancing which is the only expedient approach to cope with this situation. Low light environments can become a problem in the spread of disease because of people’s night gatherings. Especially, in summers when the global temperature is at its peak, the situation can become more critical. Mostly, in cities where people have congested homes and no proper air cross-system is available. So, they find ways to get out of their homes with their families during the night to take fresh air. In such a situation, it is necessary to take effective measures to monitor the safety distance criteria to avoid more positive cases and to control the death toll. In this paper, a deep learning-based solution is proposed for the above-stated problem. The proposed framework utilizes the you only look once v4 (YOLO v4) model for real-time object detection and the social distance measuring approach is introduced with a single motionless time of flight (ToF) camera. The risk factor is indicated based on the calculated distance and safety distance violations are highlighted. Experimental results show that the proposed model exhibits good performance with 97.84% mean average precision (mAP) score and the observed mean absolute error (MAE) between actual and measured social distance values is 1.01 cm.

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“…The autonomous driving system in this research realizes collision avoidance by integrating information from multiple sensors. Machine learning has been attracting attention to realize these information integrations and various researches have been proposed [9], [10]. Machine learning is suitable for automatically modeling behaviors that are difficult to rule manually.…”

Section: Related Workmentioning

confidence: 99%

Evaluation the Redundancy of the IoT System Based on Individual Sensing Probability

Takahashi

2020

IEICE Trans. Inf. & Syst.

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In IoT systems, data acquired by many sensors are required. However, since sensor operation depends on the actual environment, it is important to ensure sensor redundancy to improve system reliability in IoT systems. To evaluate the safety of the system, it is important to estimate the achievement probability of the function based on the sensing probability. In this research, we proposed a method to automatically generate a PRISM model from the sensor configuration of the target system and calculate and verify the function achievement probability in the assumed environment. By designing and evaluating iteratively until the target achievement probability is reached, the reliability of the system can be estimated at the initial design phase. This method reduces the possibility that the lack of reliability will be found after implementation and the redesign accompanying it will occur.

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Deep learning for self-driving cars

Cited by 168 publications

References 5 publications

A review of the technological developments for interlocking at level crossing

A review of the technological developments for interlocking at level crossing

Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera

Evaluation the Redundancy of the IoT System Based on Individual Sensing Probability

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