A Deep Learning based Virtual Sensor for Vehicle Sideslip Angle Estimation: Experimental Results
- 3 April 2018
- conference paper
- conference paper
- Published by SAE International in SAE International Journal of Advances and Current Practices in Mobility
Abstract
Modern vehicles have several active systems on board such as the Electronic Stability Control. Many of these systems require knowledge of vehicle states such as sideslip angle and yaw rate for feedback control. Sideslip angle cannot be measured with the standard sensors present in a vehicle, but it can be measured by very expensive and large optical sensors. As a result, state observers have been used to estimate sideslip angle of vehicles. The current state of the art does not present an algorithm which can robustly estimate the sideslip angle for vehicles with all-wheel drive. A deep learning network based sideslip angle observer is presented in this article for robust estimation of vehicle sideslip angle. The observer takes in the inputs from all the on board sensors present in a vehicle and it gives out an estimate of the sideslip angle. The observer is tested extensively using data which are obtained from proving grounds in high tire-road friction coefficient conditions. The data are collected from an instrumented prototype super-sports vehicle which is driven by professional test drivers. It is found that the presented observer can accurately estimate the sideslip angle for a variety of handling maneuvers.Keywords
This publication has 15 references indexed in Scilit:
- Sideslip Angle Estimation of a Formula SAE Racing VehicleSAE International Journal of Passenger Cars - Mechanical Systems, 2016
- On the vehicle sideslip angle estimation through neural networks: Numerical and experimental resultsMechanical Systems and Signal Processing, 2011
- Algorithms for Real-Time Estimation of Individual Wheel Tire-Road Friction CoefficientsIEEE/ASME Transactions on Mechatronics, 2011
- Estimation of vehicle sideslip, tire force and wheel cornering stiffnessControl Engineering Practice, 2009
- Tire-road forces estimation using extended Kalman filter and sideslip angle evaluationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Nonlinear vehicle side-slip estimation with friction adaptationAutomatica, 2007
- Vehicle velocity estimation using nonlinear observersAutomatica, 2006
- An Extended Adaptive Kalman Filter for Real-time State Estimation of Vehicle Handling DynamicsVehicle System Dynamics, 2000
- Long Short-Term MemoryNeural Computation, 1997
- Nonlinear Tire Force Estimation and Road Friction Identification: Simulation and Experiments ,Automatica, 1997