scholarly journals Real-Time Vehicle Roll Angle Estimation Based on Neural Networks in IoT Low-Cost Devices

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2188 ◽  
Author(s):  
Javier García Guzmán ◽  
Lisardo Prieto González ◽  
Jonatan Pajares Redondo ◽  
Mat Montalvo Martínez ◽  
María L. Boada
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Low Cost ◽  
Roll Angle ◽  
Angle Estimation

scholarly journals Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

2018 ◽  
Author(s):  
Javier Garcia-Guzman ◽  
Lisardo Prieto González ◽  
Jonatan Pajares Redondo ◽  
Mat Max Montalvo Martinez ◽  
María Jesús López Boada
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Low Cost ◽  
Roll Angle ◽  
Measurement Unit ◽  
Raspberry Pi ◽  
State Variables ◽  
Angle Estimation ◽  
Experimental Environment ◽  
Time Operation

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

Using AI at the Edge and Incremental Machine Learning to Process Onboard Instrument Data

2021 ◽  
Author(s):  
Nicholas Parkyn
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Real Time ◽  
Data Storage ◽  
High Performance ◽  
Heterogeneous Computing ◽  
Low Cost ◽  
Machine Intelligence ◽  
Edge Computing ◽  
Continual Learning

Emerging heterogeneous computing, computing at the edge, machine learning and AI at the edge technology drives approaches and techniques for processing and analysing onboard instrument data in near real-time. The author has used edge computing and neural networks combined with high performance heterogeneous computing platforms to accelerate AI workloads. Heterogeneous computing hardware used is readily available, low cost, delivers impressive AI performance and can run multiple neural networks in parallel. Collecting, processing and machine learning from onboard instruments data in near real-time is not a trivial problem due to data volumes, complexities of data filtering, data storage and continual learning. Little research has been done on continual machine learning which aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn from a non-stationary and never-ending stream of data. The author has applied the concept of continual learning to building a system that continually learns from actual boat performance and refines predictions previously done using static VPP data. The neural networks used are initially trained using the output from traditional VPP software and continue to learn from actual data collected under real sailing conditions. The author will present the system design, AI, and edge computing techniques used and the approaches he has researched for incremental training to realise continual learning.

scholarly journals Real-time Neural Networks Implementation Proposal for Microcontrollers

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1597
Author(s):  
Caio José B. V. Guimarães ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Intelligent Systems ◽  
Implementation Strategy ◽  
Processing Time ◽  
Mean Squared Error ◽  
Low Cost ◽  
Real Time Applications ◽  
Time Required ◽  
Process Classification

The adoption of intelligent systems with Artificial Neural Networks (ANNs) embedded in hardware for real-time applications currently faces a growing demand in fields such as the Internet of Things (IoT) and Machine to Machine (M2M). However, the application of ANNs in this type of system poses a significant challenge due to the high computational power required to process its basic operations. This paper aims to show an implementation strategy of a Multilayer Perceptron (MLP)-type neural network, in a microcontroller (a low-cost, low-power platform). A modular matrix-based MLP with the full classification process was implemented as was the backpropagation training in the microcontroller. The testing and validation were performed through Hardware-In-the-Loop (HIL) of the Mean Squared Error (MSE) of the training process, classification results, and the processing time of each implementation module. The results revealed a linear relationship between the values of the hyperparameters and the processing time required for classification, also the processing time concurs with the required time for many applications in the fields mentioned above. These findings show that this implementation strategy and this platform can be applied successfully in real-time applications that require the capabilities of ANNs.

scholarly journals Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8381
Author(s):  
Duarte Fernandes ◽  
Tiago Afonso ◽  
Pedro Girão ◽  
Dibet Gonzalez ◽  
António Silva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Low Cost ◽  
Autonomous Driving ◽  
Test Vehicle ◽  
Real Time Processing ◽  
Cloud Data ◽  
Critical Perception

Recently released research about deep learning applications related to perception for autonomous driving focuses heavily on the usage of LiDAR point cloud data as input for the neural networks, highlighting the importance of LiDAR technology in the field of Autonomous Driving (AD). In this sense, a great percentage of the vehicle platforms used to create the datasets released for the development of these neural networks, as well as some AD commercial solutions available on the market, heavily invest in an array of sensors, including a large number of sensors as well as several sensor modalities. However, these costs create a barrier to entry for low-cost solutions for the performance of critical perception tasks such as Object Detection and SLAM. This paper explores current vehicle platforms and proposes a low-cost, LiDAR-based test vehicle platform capable of running critical perception tasks (Object Detection and SLAM) in real time. Additionally, we propose the creation of a deep learning-based inference model for Object Detection deployed in a resource-constrained device, as well as a graph-based SLAM implementation, providing important considerations, explored while taking into account the real-time processing requirement and presenting relevant results demonstrating the usability of the developed work in the context of the proposed low-cost platform.

Roll angle estimation using low cost MEMS sensors for paddy field machine

2019 ◽  
Vol 158 ◽  
pp. 183-188 ◽  
Author(s):  
Lian Hu ◽  
Weiwei Yang ◽  
Jing He ◽  
Hao Zhou ◽  
Zhigang Zhang ◽  
...  
Keyword(s):  
Paddy Field ◽  
Low Cost ◽  
Roll Angle ◽  
Mems Sensors ◽  
Angle Estimation

Anti-Collision of Tower Crane via Ultrasonic Neural Networks

2014 ◽  
Vol 543-547 ◽  
pp. 1520-1524 ◽  
Author(s):  
Xi Ping Li ◽  
Li Chen Gu ◽  
Xue Qin Kou
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
High Precision ◽  
Information Fusion ◽  
Low Cost ◽  
Security Model ◽  
Sufficient Time ◽  
Ultrasonic Sensors ◽  
Tower Crane ◽  
Simulation And Experiment

The mainstream anti-collision method was still a passive security model represented by working area limitation technology, which lacked flexibility and initiative. This paper presented a practical anti-collision method which used ultrasonic sensors to obtain the accurate profile of barriers through information fusion techniques such as Elman neural networks time fusion, SOM neural networks space fusion, etc, gave operators sufficient time to take corrective action in alarm mode before a collision occurs. It was verified via simulation and experiment that the method could use in the different tower crane flexibly, detect multiple types of barriers initiatively, determine the surface outline and position of barriers accurately, meet the requirement of tower crane safety fully. Moreover, this method had characteristics of low cost, high precision, real-time.

scholarly journals Real-time estimation method of roll angle based on least squares recursion

2020 ◽  
Vol 306 ◽  
pp. 05002
Author(s):  
Chen Jingsheng ◽  
Li Chuanjun ◽  
Hu Peisen
Keyword(s):  
Least Squares ◽  
Real Time ◽  
Least Squares Method ◽  
Estimation Method ◽  
Time Estimation ◽  
Calculation Procedure ◽  
Roll Angle ◽  
Angle Estimation ◽  
Real Time Estimation ◽  
High Dynamic

The rotary missile stands a high overload during the launch and has to be powered up after launch, so it is necessary to achieve inflight alignment under high dynamic conditions. As a key technology of inflight alignment, the measurement method of roll angle has attracted more and more attention from researchers. The rotational speed of the rotary missile is very high, and most MIMUs cannot directly measure the roll angle. To solve this problem, this paper proposes a roll angle estimation method based on least squares method, analyzes its principle and derives the calculation procedure. Then on this basis, the roll angle estimation method based on least squares recursion is studied. The principle and calculation procedure of this method are deduced in detail. At last, the simulation experiment on MATLAB is carried out. The results show that this method is simple in calculation, high in accuracy and good in real-time performance, and has great application value.

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