A Hardware-in-the-Loop Simulator Based on Real Skoda Superb Vehicle and Rt-Lab/Carsim

Biroš, Milan; Kyslan, Karol; Ďurovský, František

doi:10.14311/ap.2015.55.0366

Cited by 2 publications

(1 citation statement)

References 3 publications

(3 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CarSim has been widely accepted as a substitute for real vehicle experiments. For more information about Carism software, please refer to [21,22] In this study, a light full-size car was selected as the experimental environment. The experimental data were obtained by fixed driver operation, vehicle speed, and road fric tion coefficient.…”

Section: Data Setmentioning

confidence: 99%

Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network

Kong

Wen²,

Zhao³

et al. 2021

WEVJ

View full text Add to dashboard Cite

Lateral velocity is an important parameter to characterize vehicle stability. The acquisition of lateral velocity is of great significance to vehicle stability control and the trajectory following control of autonomous vehicles. Aiming to resolve the problems of poor estimation accuracy caused by the insufficient modeling of traditional model-based methods and significant decline in performance in the case of a change in road friction coefficient, a deep learning method for lateral velocity estimation using an LSTM, long-term and short-term memory network, is designed. LSTM can well reflect the inertial characteristics of vehicles. The training data set contains sensor data under various working conditions and roads. The simulation results show that the prediction model has high accuracy in general and robustness to the change of road friction coefficient.

show abstract

Section: Data Setmentioning

confidence: 99%