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Driver Detection from Objective Criteria Describing the Driving Style of Race Car Drivers., , , and . ITSC, page 1198-1203. IEEE, (2019)Data Fusion to Develop a Driver Drowsiness Detection System with Robustness to Signal Loss., , , , and . Sensors, 14 (9): 17832-17847 (2014)Corrections to Äutomated Lane Change Featuring Re-Planning in Dynamic Environments and Sensitivity Analysis of Main Operational Parameters"., , , , and . IEEE Access, (2022)Applying deep neural networks for multi-level classification of driver drowsiness using Vehicle-based measures., , , , , and . Expert Syst. Appl., (2020)A Novel Approach for Simulation of Automotive Radar Sensors Designed for Systematic Support of Vehicle Development., and . Sensors, 23 (6): 3227 (March 2023)Measuring trust in automated driving using a multi-level approach to human factors*., , , , , and . DSD, page 410-417. IEEE, (2021)Automation in Hardware-in-the-Loop Units Development and Integration., and . QRS Companion, page 191-197. IEEE, (2019)Driver Drowsiness Classification Using Data Fusion of Vehicle-based Measures and ECG Signals., , , , and . SMC, page 451-456. IEEE, (2020)Investigations on Model Predictive Control Objectives for Motion Cueing Algorithms in Motorsport Driving Simulators., , and . IV, page 49-54. IEEE, (2021)Road friction estimation using Recursive Total Least Squares., , , and . Intelligent Vehicles Symposium, page 533-538. IEEE, (2016)