Comfort-Oriented Design of Model Predictive Control in Assisted and Autonomous Driving

2020 ◽  
Author(s):  
Sara Luciani ◽  
Angelo Bonfitto ◽  
Nicola Amati ◽  
Andrea Tonoli
Keyword(s):  
Model Predictive Control ◽  
Motion Sickness ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Front Wheel ◽  
Lateral Dynamics ◽  
Iso 2631 ◽  
Yaw Angle ◽  
Simulated Scenario

Abstract This paper presents a method based on a Model Predictive Control (MPC) aiming to optimize the passenger comforts in assisted and autonomous vehicles. The controller works on the lateral and longitudinal dynamics of the car, providing front wheel steering angle and acceleration/deceleration command. The comfort is evaluated through two indexes extracted from the ISO 2631: an equivalent acceleration aeq and a Motion Sickness Dose Value (MSDV) index. The MPC weighting parameters are designed according to the values assumed by these indexes. Specifically, each weighting parameter is changed until the most satisfying comfort evaluation and the maximum vehicle performances, in terms of lateral deviation, tracking velocity and relative yaw angle, are reached. The controller is tested numerically on a simulated scenario resulting from real GPS data obtained in a highway. The method is compared with an alternative control strategy based on the combination of a PID and a Stanley control for the longitudinal and lateral dynamics, respectively. The results demonstrate the effectiveness of the approach, leading to a low percentage of passengers can experience motion sickness.

scholarly journals Model predictive control for comfort optimization in assisted and driverless vehicles

2020 ◽  
Vol 12 (11) ◽  
pp. 168781402097453
Author(s):  
Sara Luciani ◽  
Angelo Bonfitto ◽  
Nicola Amati ◽  
Andrea Tonoli
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Vehicle Speed ◽  
Lateral Deviation ◽  
Evaluation Indexes ◽  
Longitudinal Dynamic ◽  
Comfort Evaluation ◽  
Vehicle Mass ◽  
Iso 2631 ◽  
Yaw Angle

This paper presents a method to design a Model Predictive Control to maximize the passengers’ comfort in assisted and self-driving vehicles by achieving lateral and longitudinal dynamic. The weighting parameters of the MPC are tuned off-line using a Genetic Algorithm to simultaneously maximize the control performance in the tracking of speed profile, lateral deviation and relative yaw angle and to optimize the comfort perceived by the passengers. To this end, two comfort evaluation indexes extracted by ISO 2631 are used to evaluate the amount of vibration transmitted to the passengers and the probability to experience motion sickness. The effectiveness of the method is demonstrated using simulated experiments conducted on a subcompact crossover vehicle. The control tracking performance produces errors lower than 0.1 m for lateral deviation, 0.5° for relative yaw angle and 1.5 km/h for the vehicle speed. The comfort maximization results in a low percentage of people who may experience nausea (below 5%) and in a low value of equivalent acceleration perceived by the passenger (below 0.315 [Formula: see text]“not uncomfortable” by ISO 2631). The robustness at variations of vehicle parameters, namely vehicle mass, front and rear cornering stiffness and mass distribution, is evaluated through a sensitivity analysis.

Carsickness-based design and development of a controller for autonomous vehicles to improve the comfort of occupants

2020 ◽  
Vol 235 (1) ◽  
pp. 162-176
Author(s):  
Mert Sever ◽  
Namik Zengin ◽  
Ahmet Kirli ◽  
M Selçuk Arslan
Keyword(s):  
Motion Sickness ◽  
Vestibular System ◽  
Autonomous Vehicles ◽  
Quantitative Measure ◽  
Path Following ◽  
Autonomous Driving ◽  
Performance Measure ◽  
Control Approach ◽  
Lane Change Test ◽  
Iso 2631

It is anticipated that passengers in autonomous vehicles will be more occupied with in-vehicle activities. Loss of the authority on driving and engaging in non-driving tasks could cause lower predictability of car motions. This decrease in predictability is expected to increase the sensitivity to carsickness. It appears that it is crucial to develop controllers for autonomous driving with the capability of improving passenger comfort by reducing carsickness. In this regard, it can be asked how the motion variables can be used for the minimization of a carsickness-related measure, while the vehicle is required to follow a given path. In this study, an optimal control approach is being proposed to minimize a quantitative measure of carsickness. In order to address carsickness during autonomous maneuvers, the well-known motion sickness dose value formulation in ISO 2631-1 is augmented with horizontal direction motion components to define a performance measure. The performance measure includes the motion sensed in vestibular system rather than the motion occurring in the vehicle itself. Therefore, mathematical model of the vestibular system is included in the design of controller. Effects of acceleration and jerk are included in performance measure simultaneously. Control oriented linear parameter varying vehicle model is developed to design the path following controller. By means of simulation studies in which path following control is implemented, motion sickness dose values of the controlled vehicle are examined. It is shown by a regular lane change test at various speeds that the proposed controller, which seeks the minimization of the motion sickness dose value, achieves a reduction of the acceleration and jerk felt by a passenger, while the vehicle follows the given path.

scholarly journals LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110195
Author(s):  
Sorin Grigorescu ◽  
Cosmin Ginerica ◽  
Mihai Zaha ◽  
Gigel Macesanu ◽  
Bogdan Trasnea
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Process Model ◽  
Network Performance ◽  
Vision System ◽  
Nonlinear Model Predictive Control ◽  
Scaled Model

In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.

scholarly journals Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving

2018 ◽  
Vol 34 (6) ◽  
pp. 1603-1622 ◽  
Author(s):  
Grady Williams ◽  
Paul Drews ◽  
Brian Goldfain ◽  
James M. Rehg ◽  
Evangelos A. Theodorou
Keyword(s):  
Control Theory ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Driving ◽  
Theoretic Model ◽  
Information Theoretic
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