scholarly journals Intelligent Tire Sensor-Based Real-Time Road Surface Classification Using an Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3233
Author(s):  
Dongwook Lee ◽  
Ji-Chul Kim ◽  
Mingeuk Kim ◽  
Hanmin Lee
Keyword(s):  
Neural Network ◽  
Control Systems ◽  
Real Time ◽  
Vertical Axis ◽  
Autonomous Driving ◽  
Classification Algorithm ◽  
Road Surface ◽  
Electronic Control ◽  
Surface Classification ◽  
Acceleration Signals

Vehicles today have many advanced driver assistance control systems that improve vehicle safety and comfort. With the development of more sophisticated vehicle electronic control and autonomous driving technology, the need and effort to estimate road surface conditions is increasing. In this paper, a real-time road surface classification algorithm, based on a deep neural network, is developed using a database collected through an intelligent tire sensor system with a three-axis accelerometer installed inside the tire. Two representative types of network, fully connected neural network (FCNN) and convolutional neural network (CNN), are learned with each of the three-axis acceleration sensor signals, and their performances were compared to obtain an optimal learning network result. The learning results show that the road surface type can be classified in real-time with sufficient accuracy when the longitudinal and vertical axis acceleration signals are trained with the CNN. In order to improve classification accuracy, a CNN with multiple input that can simultaneously learn 2-axis or 3-axis acceleration signals is suggested. In addition, by analyzing how the accuracy of the network is affected by number of classes and length of input data, which is related to delay of classification, the appropriate network can be selected according to the application. The proposed real-time road surface classification algorithm is expected to be utilized with various vehicle electronic control systems and makes a contribution to improving vehicle performance.

Fuzzy PID Control of ABS Based on Real-Time Road Surface Identification

2014 ◽  
Vol 597 ◽  
pp. 380-383
Author(s):  
Bo Wang ◽  
Ping Ping Lu ◽  
Hsin Guan ◽  
Jie Jing
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Road Surface ◽  
Active Safety ◽  
Identification Algorithm ◽  
Electronic Control ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Slip Ratio ◽  
Optimal Slip Ratio

Road surface identification is of great significance in vehicle active safety electronic control systems. This paper proposes a real-time road surface identification algorithm on the basis of the estimated instantaneous road adhesive coefficient. Based on Fuzzy-PID controller and automatic road surface identification, the actual slip ratio can be controlled at the optimal slip ratio precisely, which can promote the ABS and braking performance largely. The braking simulation tests are conducted on pre-set varying road surface conditions. And the results show that the identified results are in good agreement with the pre-set road surface, the proposed algorithm can be conveniently used for various active safety electronic control systems of vehicles.

Road Surface Classification Based on Radar Imaging Using Convolutional Neural Network

2021 ◽  
pp. 1-1
Author(s):  
Shahrzad Minooee Sabery ◽  
Aleksandr Bystrov ◽  
Peter Gardner ◽  
Ana Stroescu ◽  
Marina Gashinova
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Radar Imaging ◽  
Road Surface ◽  
Surface Classification

scholarly journals Real Time Environmental Classification Algorithm Using Neural Network for Hearing Aids

2013 ◽  
Vol 34 (1) ◽  
pp. 8-13 ◽  
Author(s):  
Sangwan Seo ◽  
Sunhyun Yook ◽  
Kyoung Won Nam ◽  
Jonghee Han ◽  
See Youn Kwon ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Hearing Aids ◽  
Classification Algorithm ◽  
Environmental Classification

scholarly journals Image based Road Surface Classification Method using CNN

2019 ◽  
Vol 8 (2S6) ◽  
pp. 158-162
Keyword(s):  
Data Augmentation ◽  
Autonomous Driving ◽  
Input Image ◽  
Road Surface ◽  
Learning Performance ◽  
Limited Data ◽  
Image Information ◽  
Optimal Network ◽  
Experimental Vehicle ◽  
Surface Classification

Road detection and road surface classification in autonomous driving are the most basic and important issues. In this paper, we propose a data augmentation method for road surface classification using image information. We design an optimal network that can classify the type of road surface from the input image information and propose a data increase technique that can efficiently judge by using limited data to improve learning performance. To verify the proposed methods, many running images were used on the Internet. Experimental vehicle was developed and applied to verify the developed networks and it shows that they operate accurately in real time.

scholarly journals Traffic Sign Classification Using Convolutional Neural Network

2021 ◽  
pp. 01-10
Author(s):  
Pranav Kale ◽  
Mayuresh Panchpor ◽  
Saloni Dingore ◽  
Saloni Gaikwad ◽  
Prof. Dr. Laxmi Bewoor
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Autonomous Driving ◽  
Time Data ◽  
Traffic Sign ◽  
Real World Data ◽  
Data Set ◽  
Proposed Model ◽  
Validation Set

In today's world, deep learning fields are getting boosted with increasing speed. Lot of innovations and different algorithms are being developed. In field of computer vision, related to autonomous driving sector, traffic signs play an important role to provide real time data of an environment. Different algorithms were developed to classify these Signs. But performance still needs to improve for real time environment. Even the computational power required to train such model is high. In this paper, Convolutional Neural Network model is used to Classify Traffic Sign. The experiments are conducted on a real-world data set with images and videos captured from ordinary car driving as well as on GTSRB dataset [15] available on Kaggle. This proposed model is able to outperform previous models and resulted with accuracy of 99.6% on validation set. This idea has been granted Innovation Patent by Australian IP to Authors of this Research Paper. [24]

Analog Neural Network Implementation for a Real-Time Surface Classification Application

2008 ◽  
Vol 8 (8) ◽  
pp. 1413-1421 ◽  
Author(s):  
Laurent Gatet ◽  
HÉlÈne Tap-Beteille ◽  
Marc Lescure
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Surface Classification ◽  
Analog Neural Network

scholarly journals Low-Cost FPGA-Based Electronic Control Unit for Vehicle Control Systems

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1834 ◽  
Author(s):  
Javier Pérez Fernández ◽  
Manuel Alcázar Vargas ◽  
Juan M. Velasco García ◽  
Juan A. Cabrera Carrillo ◽  
Juan J. Castillo Aguilar
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Electronic Control Unit ◽  
Low Cost ◽  
Control Unit ◽  
Electronic System ◽  
Control Algorithms ◽  
Electronic Control ◽  
Binary File ◽  
Arm Processor

The development of new control algorithms in vehicles requires high economic resources, mainly due to the use of generic real-time instrumentation and control systems. In this work, we proposed a low-cost electronic control unit (ECU) that could be used for both development and implementation. The proposed electronic system used a hybrid system on chip (SoC) between a field-programmable gate array (FPGA) and an Advanced RISC (reduced instruction set computer) Machine (ARM) processor that allowed the execution of parallel tasks, fulfilling the real-time requirements that vehicle controls demand. Another feature of the proposed electronic system was the recording of measured data, allowing the performance of the implemented algorithm to be evaluated. All this was achieved by using modular programming that, without the need for a real-time operating system, executed the different tasks to be performed, exploiting the parallelism offered by the FPGA as well as the dual core of the ARM processor. This methodology facilitates the transition between the designing, testing, and implementation stages in the vehicle. In addition, our system is programmed with a single binary file that integrates the code of all processors as well as the hardware description of the FPGA, which speeds up the updating process. In order to validate and demonstrate the performance of the proposed electronic system as a tool for the development and implementation of control algorithms in vehicles, a series of tests was carried out on a test bench. Different traction control system (TCS) algorithms were implemented and the results were compared.

scholarly journals Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles

2021 ◽  
Vol 309 ◽  
pp. 01167
Author(s):  
G. Ramesh ◽  
J. Praveen
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Path Planning ◽  
Real Time ◽  
Power Management ◽  
Transfer Learning ◽  
Electric Vehicles ◽  
Recurrent Neural Network ◽  
Autonomous Driving

An electric vehicle with autonomous driving is a possibility provided technology innovations in multi-disciplinary approach. Electric vehicles leverage environmental conditions and are much desired in the contemporary world. Another great possibility is to strive for making the vehicle to drive itself (autonomous driving) provided instructions. When the two are combined, it leads to a different dimension of environmental safety and technology driven driving that has many pros and cons as well. It is still in its infancy and there is much research to be carried out. In this context, this paper is aimed at building an Artificial Intelligence (AI) framework that has dual goal of “monitoring and regulating power usage” and facilitating autonomous driving with technology-driven and real time knowledge required. A methodology is proposed with multiple deep learning methods. For instance, deep learning is used for localization of vehicle, path planning at high level and path planning for low level. Apart from this, there is reinforcement learning and transfer learning to speed up the process of gaining real time business intelligence. To facilitate real time knowledge discovery from given scenarios, both edge and cloud resources are appropriately exploited to benefit the vehicle as driving safety is given paramount importance. There is power management module where modular Recurrent Neural Network is used. Another module known as speed control is used to have real time control over the speed of the vehicle. The usage of AI framework makes the electronic and autonomous vehicles realize unprecedented possibilities in power management and safe autonomous driving. Key words: Artificial Intelligence Autonomous Driving Recurrent Neural Network Transfer Learning

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