Real-Time Roll Angle Estimation for Two-Wheeled Vehicles

2012 ◽  
Author(s):  
Roberto Lot ◽  
Vittore Cossalter ◽  
Matteo Massaro
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Simulated Data ◽  
Time Estimation ◽  
Real Data ◽  
Original Method ◽  
Roll Angle ◽  
Collision Warning ◽  
Multibody Model ◽  
Wheeled Vehicles

An original method for the real-time estimation of the roll angle using low-cost sensors in two-wheeled vehicles is proposed. The roll angle greatly affects the dynamics of single-track vehicles and its estimation is essential in control systems such as ABS, Traction Control, as well as Curve and Collision Warning, or even active suspensions. The proposed method uses a non-linear Kalman filter, its performances are assessed by using both a set of simulated data from a multibody model and a set of real data collected on an instrumented test vehicle.

Ship As a Wave Buoy: Using Simulated Data to Train Neural Networks for Real Time Estimation of Relative Wave Direction

2019 ◽  
Author(s):  
Bart Mak ◽  
Bülent Düz
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
General Model ◽  
Simulated Data ◽  
Time Estimation ◽  
Measured Data ◽  
Wave Direction ◽  
Sea State ◽  
Digital Twin ◽  
Extended Training

Abstract Being able to give real time on-board advice, without depending on extensive sets of measured data, is the ultimate goal of the digital twin concept. Ideally, the models used in a digital twin only rely on current in-service data, although they have been built using simulated and possibly some measured data. Working with just the 6-DOF motions of a ship, can the local sea state reliably be estimated using the digital twin concept? Does a general model exist to do so, without the need to measure or simulate the particular ship? In this paper, we discuss how simulations of an advancing ship, subjected to various sea states, can be used to estimate the relative wave direction from in-service motion measurements of the corresponding ship. Various types of neural networks are used and evaluated with simulated data and measured data. In order to study the generalization power of the neural networks, a range of ships has been simulated, with varying lengths, drafts and geometries. Neural networks have been trained on selections of the ships in this extended training set and evaluated on the remaining ships. Results show that the developed neural networks give a remarkable performance in simulation data. Furthermore, generalization over geometry is very good, opening the door to train a general model for estimating sea state characteristics. Using the same model for in-service measurements does not perform well enough yet and further research is required. The paper will include discussion on possible causes for this performance gap and some promising ideas for future work.

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.

scholarly journals Estimating Illumination Direction for Augmented Reality in Real-Time by using Low-Cost Sensors

2017 ◽  
Vol 17 (02) ◽  
pp. e20 ◽  
Author(s):  
Kevin E. Soulier ◽  
Matías Nicolás Selzer ◽  
Martín Leonardo Larrea
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Low Cost ◽  
Time Estimation ◽  
The Real ◽  
Virtual Objects ◽  
Illumination Direction ◽  
Real Objects ◽  
Real Time Estimation ◽  
Commercial Field

In recent years, Augmented Reality has become a very popular topic, both as a research and commercial field. This trend has originated with the use of mobile devices as computational core and display. The appearance of virtual objects and their interaction with the real world is a key element in the success of an Augmented Reality software. A common issue in this type of software is the visual inconsistency between the virtual and real objects due to wrong illumination. Although illumination is a common research topic in Computer Graphics, few studies have been made about real time estimation of illumination direction. In this work we present a low-cost approach to detect the direction of the environment illumination, allowing the illumination of virtual objects according to the real light of the ambient, improving the integration of the scene. Our solution is open-source, based on Arduino hardware and the presented system was developed on Android.

Generalized Ordinal Learning Framework (GOLF) for Decision Making with Future Simulated Data

2019 ◽  
Vol 36 (06) ◽  
pp. 1940011
Author(s):  
Giulia Pedrielli ◽  
K. Selcuk Candan ◽  
Xilun Chen ◽  
Logan Mathesen ◽  
Alireza Inanalouganji ◽  
...  
Keyword(s):  
Dynamical System ◽  
Decision Making ◽  
Dynamical Systems ◽  
Real Time ◽  
Simulated Data ◽  
Simulation Models ◽  
Real Data ◽  
Learning Framework ◽  
Main Challenge ◽  
Specific Scenario

Real-time decision making has acquired increasing interest as a means to efficiently operating complex systems. The main challenge in achieving real-time decision making is to understand how to develop next generation optimization procedures that can work efficiently using: (i) real data coming from a large complex dynamical system, (ii) simulation models available that reproduce the system dynamics. While this paper focuses on a different problem with respect to the literature in RL, the methods proposed in this paper can be used as a support in a sequential setting as well. The result of this work is the new Generalized Ordinal Learning Framework (GOLF) that utilizes simulated data interpreting them as low accuracy information to be intelligently collected offline and utilized online once the scenario is revealed to the user. GOLF supports real-time decision making on complex dynamical systems once a specific scenario is realized. We show preliminary results of the proposed techniques that motivate the authors in further pursuing the presented ideas.

scholarly journals Real Time Estimation of Population Receptive Fields Using Gradient Descent

2017 ◽  
Author(s):  
Mario Senden
Keyword(s):  
Real Time ◽  
Gradient Descent ◽  
Receptive Fields ◽  
Simulated Data ◽  
Time Estimation ◽  
Ground Truth ◽  
Good Correspondence ◽  
Mapping Procedure ◽  
Model Free ◽  
Exact Shape

AbstractA real-time population receptive field mapping procedure based on gradient descent is proposed. Model-free receptive fields produced by the algorithm are evaluated in context of simulated data exhibiting different levels of temporally autocorrelated noise and spatial point spread. As with any model-free approach, the exact shape of receptive fields produced by the real-time algorithm depends on the stimulus. Nevertheless, estimated receptive fields show good correspondence with ground-truth receptive fields in terms of both position and size. Furthermore, fitting a parametric model to the previously obtained estimates approximates the exact shape of the true underlying receptive fields well.

scholarly journals The genome polishing tool POLCA makes fast and accurate corrections in genome assemblies

2019 ◽  
Author(s):  
Aleksey V. Zimin ◽  
Steven L. Salzberg
Keyword(s):  
Error Rate ◽  
Low Cost ◽  
Simulated Data ◽  
Real Data ◽  
Hybrid Strategy ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Genome Assemblies ◽  
Polishing Tool

AbstractThe introduction of third-generation DNA sequencing technologies in recent years has allowed scientists to generate dramatically longer sequence reads, which when used in whole-genome sequencing projects have yielded better repeat resolution and far more contiguous genome assemblies. While the promise of better contiguity has held true, the relatively high error rate of long reads, averaging 8–15%, has made it challenging to generate a highly accurate final sequence. Current long-read sequencing technologies display a tendency toward systematic errors, in particular in homopolymer regions, which present additional challenges. A cost-effective strategy to generate highly contiguous assemblies with a very low overall error rate is to combine long reads with low-cost short-read data, which currently have an error rate below 0.5%. This hybrid strategy can be pursued either by incorporating the short-read data into the early phase of assembly, during the read correction step, or by using short reads to “polish” the consensus built from long reads. In this report, we present the assembly polishing tool POLCA (POLishing by Calling Alternatives) and compare its performance with two other popular polishing programs, Pilon and Racon. We show that on simulated data POLCA is more accurate than Pilon, and comparable in accuracy to Racon. On real data, all three programs show similar performance, but POLCA is consistently much faster than either of the other polishing programs.

A New Real-time Method for Sailboat Performance Estimation Based on Leeway Modeling

2013 ◽  
Author(s):  
Ronan Douguet ◽  
Jean-Philippe Diguet ◽  
Johann Laurent ◽  
Yann Riou
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Time Estimation ◽  
Real Data ◽  
Performance Estimation ◽  
Sensor Data ◽  
Ocean Current ◽  
Loop Model ◽  
Specific Step ◽  
Real Time Estimation

This paper presents new methods for real time estimation of leeway and ocean current, which are based on boat displacements. We propose two solutions that rely on several types of Kalman filters. The first one uses the empirical leeway definition and allows finding the key parameter of this formula. The solution works properly if the error of the formula of leeway remains limited. The second solution takes advantage of an additional sensor and we compare three methods to linearize boat displacements, which are based on a closed-loop model including cascaded filters. These methods are tested on simulation and on real data collected with a maxi multihull. The results first validate the use of a DVL sensor for leeway estimation but also show that it requires the implementation of a complex and specific step of signal processing. Secondly our study demonstrates the relevancy of the closed-loop approach and shows that a solution, based on UKF filters, provides a relevant method to cope with accuracy and stability in case of sensor data outage.

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