A driver model is designed which relates the driver's action to his perception, driving experience, and preferences over a wide range of possible traffic situations. The basic idea behind the work is that the human uses his sensory perception and his expert knowledge to predict the vehicle's future behavior for the next few seconds (prediction model). At a certain sampling rate the vehicle's future motion is optimized using this prediction model, in order to meet certain objectives. The human tries to follow this optimal behavior using a compensatory controller. Based on this hypothesis, human vehicle driving is modeled by a hierarchical controller. A repetitive nonlinear optimization is employed to plan the vehicle's future motion (trajectory planning task), using an SQP algorithm. This is combined with a PID tracking control to minimize its deviations. The trajectory planning scheme is experimentally verified for undisturbed driving situations employing various objectives, namely ride comfort, lane keeping, and minimized speed variation. The driver model is then applied to study path planning during curve negotiation under various preferences. A highly dynamic avoidance maneuver (standardized ISO double lane change) is then simulated to investigate the overall stability of the closed loop vehicle/driver system.
Modeling Human Vehicle Driving by Model Predictive Online Optimization
Vehicle System Dynamics ; 35 , 1 ; 19-53
2001-01-01
Article (Journal)
Electronic Resource
Unknown
Modeling human vehicle driving by model predictive online optimization
Automotive engineering | 2001
|Modeling Human Vehicle Driving by Model Predictive Online Optimization
Online Contents | 2001
|Modeling human vehicle driving by model predictive online optimization
Tema Archive | 2001
|Robust Model Predictive Control Based on Online Optimization
British Library Online Contents | 2013
|PREDICTIVE POWERTRAIN LIMIT STRATEGY FOR AUTONOMOUS DRIVING VEHICLE/AUTOMATIC DRIVING VEHICLE
European Patent Office | 2018
|