Next Article in Journal
Improved Active Disturbance Rejection Control for Trajectory Tracking Control of Lower Limb Robotic Rehabilitation Exoskeleton
Previous Article in Journal
Ion-Selective Carbon Nanotube Field-Effect Transistors for Monitoring Drug Effects on Nicotinic Acetylcholine Receptor Activation in Live Cells
Previous Article in Special Issue
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles
Open AccessArticle

Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach

1
Computer Science Department, Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Madrid, Spain
2
Mechanical Engineering Department, Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Madrid, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3679; https://doi.org/10.3390/s20133679
Received: 1 June 2020 / Revised: 25 June 2020 / Accepted: 29 June 2020 / Published: 30 June 2020
(This article belongs to the Special Issue Sensors and Sensor's Fusion in Autonomous Vehicles)
Presently, autonomous vehicles are on the rise and are expected to be on the roads in the coming years. In this sense, it becomes necessary to have adequate knowledge about its states to design controllers capable of providing adequate performance in all driving scenarios. Sideslip and roll angles are critical parameters in vehicular lateral stability. The later has a high impact on vehicles with an elevated center of gravity, such as trucks, buses, and industrial vehicles, among others, as they are prone to rollover. Due to the high cost of the current sensors used to measure these angles directly, much of the research is focused on estimating them. One of the drawbacks is that vehicles are strong non-linear systems that require specific methods able to tackle this feature. The evolution in Artificial Intelligence models, such as the complex Artificial Neural Network architectures that compose the Deep Learning paradigm, has shown to provide excellent performance for complex and non-linear control problems. In this paper, the authors propose an inexpensive but powerful model based on Deep Learning to estimate the roll and sideslip angles simultaneously in mass production vehicles. The model uses input signals which can be obtained directly from onboard vehicle sensors such as the longitudinal and lateral accelerations, steering angle and roll and yaw rates. The model was trained using hundreds of thousands of data provided by Trucksim® and validated using data captured from real driving maneuvers using a calibrated ground truth device such as VBOX3i dual-antenna GPS from Racelogic®. The use of both Trucksim® software and the VBOX measuring equipment is recognized and widely used in the automotive sector, providing robust data for the research shown in this article. View Full-Text
Keywords: sensor fusion; deep Learning based estimator; vehicle dynamics; roll angle; sideslip angle sensor fusion; deep Learning based estimator; vehicle dynamics; roll angle; sideslip angle
Show Figures

Figure 1

MDPI and ACS Style

González, L.P.; Sánchez, S.S.; Garcia-Guzman, J.; Boada, M.J.L.; Boada, B.L. Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach. Sensors 2020, 20, 3679.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop