An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance

Mata-Carballeira, Óscar; Gutiérrez-Zaballa, Jon; Campo, I. del; Martinez, V. Sanchez

doi:10.3390/s19184011

Cited by 17 publications

(11 citation statements)

References 54 publications

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“…These devices enable highly differentiated designs for a wide range of embedded applications, including medical endoscopes, professional cameras, machine vision, and many others [13][14][15][16]. In particular, the Xilinx Zynq-7000 XC7Z045 SoC, which contains Kintex-7 based PL alongside the ARM cores, is being used for diverse portable industrial applications [17][18][19].…”

Section: Cross-platform Comparisonmentioning

confidence: 99%

iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing

Tekleyohannes

Rybalkin

Ghaffar

et al. 2021

Int J Parallel Prog

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In recent years, $$\hbox {optical character recognition (OCR)}$$ optical character recognition (OCR) systems have been used to digitally preserve historical archives. To transcribe historical archives into a machine-readable form, first, the documents are scanned, then an $$\hbox {OCR}$$ OCR is applied. In order to digitize documents without the need to remove them from where they are archived, it is valuable to have a portable device that combines scanning and $$\hbox {OCR}$$ OCR capabilities. Nowadays, there exist many commercial and open-source document digitization techniques, which are optimized for contemporary documents. However, they fail to give sufficient text recognition accuracy for transcribing historical documents due to the severe quality degradation of such documents. On the contrary, the anyOCR system, which is designed to mainly digitize historical documents, provides high accuracy. However, this comes at a cost of high computational complexity resulting in long runtime and high power consumption. To tackle these challenges, we propose a low power energy-efficient accelerator with real-time capabilities called iDocChip, which is a configurable hybrid hardware-software programmable $$\hbox {System-on-Chip (SoC)}$$ System-on-Chip (SoC) based on anyOCR for digitizing historical documents. In this paper, we focus on one of the most crucial processing steps in the anyOCR system: Text and Image Segmentation, which makes use of a multi-resolution morphology-based algorithm. Moreover, an optimized $$\hbox {FPGA}$$ FPGA -based hybrid architecture of this anyOCR step along with its optimized software implementations are presented. We demonstrate our results on multiple embedded and general-purpose platforms with respect to runtime and power consumption. The resulting hardware accelerator outperforms the existing anyOCR by 6.2$$\times$$ × , while achieving 207$$\times$$ × higher energy-efficiency and maintaining its high accuracy.

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Section: Cross-platform Comparisonmentioning

confidence: 99%

iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing

Tekleyohannes

Rybalkin

Ghaffar

et al. 2021

Int J Parallel Prog

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show abstract

“…As a result, these hybrid SoCs enable highly differentiated designs for a wide range of embedded applications, including medical endoscopes, professional cameras, machine vision, and many others [ 36 , 37 , 38 , 39 ]. In particular, the Xilinx Zynq-7045 SoC, which contains the Kintex-7 based PL alongside the ARM cores, is used for diverse portable industrial applications [ 40 , 41 , 42 ]. Hence, we have selected this Xilinx Zynq SoC as a target platform to design the portable OCR device.…”

Section: Related Workmentioning

confidence: 99%

iDocChip: A Configurable Hardware Accelerator for an End-to-End Historical Document Image Processing

et al. 2021

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In recent years, there has been an increasing demand to digitize and electronically access historical records. Optical character recognition (OCR) is typically applied to scanned historical archives to transcribe them from document images into machine-readable texts. Many libraries offer special stationary equipment for scanning historical documents. However, to digitize these records without removing them from where they are archived, portable devices that combine scanning and OCR capabilities are required. An existing end-to-end OCR software called anyOCR achieves high recognition accuracy for historical documents. However, it is unsuitable for portable devices, as it exhibits high computational complexity resulting in long runtime and high power consumption. Therefore, we have designed and implemented a configurable hardware-software programmable SoC called iDocChip that makes use of anyOCR techniques to achieve high accuracy. As a low-power and energy-efficient system with real-time capabilities, the iDocChip delivers the required portability. In this paper, we present the hybrid CPU-FPGA architecture of iDocChip along with the optimized software implementations of the anyOCR. We demonstrate our results on multiple platforms with respect to runtime and power consumption. The iDocChip system outperforms the existing anyOCR by 44× while achieving 2201× higher energy efficiency and a 3.8% increase in recognition accuracy.

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“…There are many indicators that can be used to characterize the driving style of drivers in car-following. Except for basic kinematic indicators, such as spacing, speed, or relative speed, time-based variables such as time headway (TH) and time-to-collision (TTC) have also been accepted for characterizing driving styles [ 47 , 48 ]. In the study conducted by Liu et al [ 49 ], mean TH and mean TH when braking were adopted as features to classify drivers into different driving styles.…”

Section: Data Descriptionmentioning

confidence: 99%

Modeling Car-Following Behaviors and Driving Styles with Generative Adversarial Imitation Learning

Zhou

Wang

et al. 2020

Sensors

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Building a human-like car-following model that can accurately simulate drivers’ car-following behaviors is helpful to the development of driving assistance systems and autonomous driving. Recent studies have shown the advantages of applying reinforcement learning methods in car-following modeling. However, a problem has remained where it is difficult to manually determine the reward function. This paper proposes a novel car-following model based on generative adversarial imitation learning. The proposed model can learn the strategy from drivers’ demonstrations without specifying the reward. Gated recurrent units was incorporated in the actor-critic network to enable the model to use historical information. Drivers’ car-following data collected by a test vehicle equipped with a millimeter-wave radar and controller area network acquisition card was used. The participants were divided into two driving styles by K-means with time-headway and time-headway when braking used as input features. Adopting five-fold cross-validation for model evaluation, the results show that the proposed model can reproduce drivers’ car-following trajectories and driving styles more accurately than the intelligent driver model and the recurrent neural network-based model, with the lowest average spacing error (19.40%) and speed validation error (5.57%), as well as the lowest Kullback-Leibler divergences of the two indicators used for driving style clustering.

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An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance

Cited by 17 publications

References 54 publications

iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing

iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing

iDocChip: A Configurable Hardware Accelerator for an End-to-End Historical Document Image Processing

Modeling Car-Following Behaviors and Driving Styles with Generative Adversarial Imitation Learning

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