scholarly journals Human-like trapezoidal steering angle model on two-lane urban curves

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986761 ◽  
Author(s):  
Haobin Jiang ◽  
Jie Zhou ◽  
Aoxue Li ◽  
Xinchen Zhou ◽  
Shidian Ma
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Rapid Development ◽  
Vehicle Speed ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Steering Angle ◽  
The Road ◽  
Human Likeness ◽  
Simulation Results

With the rapid development of automated vehicles, there is currently a significant amount of automated driving research. Giving automated vehicles capabilities similar to those of experienced drivers will allow them to share the road harmoniously with manned vehicles, especially on two-lane urban curves. To represent the steering behavior of experienced drivers, a series of curve feature distances are proposed, which is determined by multi-regression. These series of curve feature distances are used to generate a trapezoidal steering angle model which imitates the steering behavior of the experienced test drivers. To verify the feasibility and human-likeness of the proposed trapezoidal steering angle model, the model is used with constant vehicle speed to plan a human-like trajectory which is tracked using model predictive control. The simulation results show that the proposed trapezoidal steering angle model is human-like and could be used to give automated vehicles human-like driving capability when driving on two-lane curves.

A Model Predictive Control Strategy for Lateral and Longitudinal Dynamics in Autonomous Driving

2020 ◽  
Author(s):  
Irfan Khan ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Nicola Amati
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Autonomous Driving ◽  
Maximum Speed ◽  
Reference Trajectory ◽  
Vehicle Speed ◽  
The Road ◽  
Speed Regulation ◽  
Road Geometry

Abstract This paper presents a controller dedicated to the lateral and longitudinal vehicle dynamics control for autonomous driving. The proposed strategy exploits a Model Predictive Control strategy to perform lateral guidance and speed regulation. To this end, the algorithm controls the steering angle and the throttle and brake pedals for minimizing the vehicle’s lateral deviation and relative yaw angle with respect to the reference trajectory, while the vehicle speed is controlled to drive at the maximum acceptable longitudinal speed considering the adherence and legal speed limits. The technique exploits data computed by a simulated camera mounted on the top of the vehicle while moving in different driving scenarios. The longitudinal control strategy is based on a reference speed generator, which computes the maximum speed considering the road geometry and lateral motion of the vehicle at the same time. The proposed controller is tested in highway, interurban and urban driving scenarios to check the performance of the proposed method in different driving environments.

scholarly journals Vehicle optimal road departure prevention via model predictive control

2017 ◽  
Vol 231 (7) ◽  
pp. 952-962 ◽  
Author(s):  
Hongliang Yuan ◽  
Yangyan Gao ◽  
Timothy J Gordon
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Speed ◽  
Weather Conditions ◽  
Particle Model ◽  
Vehicle Speed ◽  
Safety Control ◽  
The Road ◽  
Adverse Weather ◽  
Actuator Control

This article addresses the problem of road departure prevention using integrated brake control. The scenario considered is when a high-speed vehicle leaves the highway on a curve and enters the shoulder or another lane, owing to excessive speed or a reduction in the friction of the road due to adverse weather conditions. In such a scenario, the vehicle speed is too high for the available tyre–road friction and road departure is inevitable; however, its effect can be minimized with an optimal braking strategy. To achieve online implementation, the task is formulated as a receding horizon optimization problem and solved in a linear model predictive control (MPC) framework. In this formulation, a nonlinear tyre model is adopted in order to work properly at the friction limits. The optimization results are close to those obtained previously using a particle model optimization, parabolic path reference (PPR), coupled to a control algorithm, the modified Hamiltonian algorithm (MHA), specifically designed to operate at the vehicle friction limits. This shows that the MPC formulation may be equally effective for vehicle control at the friction limits. The major difference here, compared with the earlier PPR/MHA control formulation, is that the proposed MPC strategy directly generates an optimal brake sequence, while PPR provides an optimal reference first, then MHA responds to the reference to give closed-loop actuator control. The presented MPC approach has the potential for use in future vehicle systems as part of the overall active safety control to improve overall vehicle agility and safety.

scholarly journals Integration of a Model Predictive Control with a Fast Energy Management Strategy for a Hybrid Powertrain of a Connected and Automated Vehicle

2021 ◽  
Vol 12 (3) ◽  
pp. 159
Author(s):  
Enrico Landolfi ◽  
Francesco Junior Minervini ◽  
Nicola Minervini ◽  
Vincenzo De De Bellis ◽  
Enrica Malfi ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Energy Management ◽  
Predictive Control ◽  
Management Strategy ◽  
Transport Sector ◽  
Energy Management Strategy ◽  
Steering Angle ◽  
Hybrid Powertrain ◽  
The Road

In the years to come, Connected and Automated Vehicles (CAVs) are expected to substantially improve the road safety and environmental impact of the road transport sector. The information from the sensors installed on the vehicle has to be properly integrated with data shared by the road infrastructure (smart road) to realize vehicle control, which preserves traffic safety and fuel/energy efficiency. In this context, the present work proposes a real-time implementation of a control strategy able to handle simultaneously motion and hybrid powertrain controls. This strategy features a cascade of two modules, which were implemented through the model-based design approach in MATLAB/Simulink. The first module is a Model Predictive Control (MPC) suitable for any Hybrid Electric Vehicle (HEV) architecture, acting as a high-level controller featuring an intermediate layer between the vehicle powertrain and the smart road. The MPC handles both the lateral and longitudinal vehicle dynamics, acting on the wheel torque and steering angle at the wheels. It is based on a simplified, but complete ego-vehicle model, embedding multiple functionalities such as an adaptive cruise control, lane keeping system, and emergency electronic brake. The second module is a low-level Energy Management Strategy (EMS) of the powertrain realized by a novel and computationally light approach, which is based on the alternative vehicle driving by either a thermal engine or electric unit, named the Efficient Thermal Electric Skipping Strategy (ETESS). The MPC provides the ETESS with a torque request handled by the EMS module, aiming at minimizing the fuel consumption. The MPC and ETESS ran on the same Microcontroller Unit (MCU), and the methodology was verified and validated by processor-in-the-loop tests on the ST Microelectronics board NUCLEO-H743ZI2, simulating on a PC-host the smart road environment and a car-following scenario. From these tests, the ETESS resulted in being 15-times faster than than the well-assessed Equivalent Consumption Minimization Strategy (ECMS). Furthermore, the execution time of both the ETESS and MPC was lower than the typical CAN cycle time for the torque request and steering angle (10 ms). Thus, the obtained result can pave the way to the implementation of additional real-time control strategies, including decision-making and motion-planning modules (such as path-planning algorithms and eco-driving strategies).

scholarly journals Automated Vehicle’s Overtaking Maneuver with Yielding to Oncoming Vehicles in Urban Area Based on Model Predictive Control

2021 ◽  
Vol 11 (19) ◽  
pp. 9003
Author(s):  
Yan Zhang ◽  
Xun Shen ◽  
Pongsathorn Raksincharoensak
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Rapid Development ◽  
Steering Wheel ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Passenger Cars ◽  
Safety Assurance ◽  
Safety Constraints ◽  
Reasonable Behavior

The rapid development of automated driving technology has brought many emerging technologies. The collision avoidance (CA) function by braking and/or steering maneuver of advanced driver assistance systems (ADAS), which contributes to the improvement of the safety of automated vehicles, has been playing an important role in recent modern passenger cars and commercial vehicles. When an automated vehicle needs to avoid multiple obstacles at the same time, consuming travel time and safety assurance of CA need to be carefully considered especially in the case related to unpredictable motion of obstacles. This paper proposes a feasible solution to this situation by controlling speed and the steering wheel angle. The proposed motion re-planning based on post-encroachment time (PET) provides a judgment of a function which calculates the possibility of unavoidable road accidents. Then the path re-planning layer of a novel two-layer model predictive control (TL-MPC) will re-plan a local trajectory and give a reference acceleration. Finally, the path tracking layer outputs steering wheel angle to follow the trajectory under the premise of ensuring safety constraints. The proposed control system is evaluated by co-simulations of MATLAB/Simulink and CarSim software. The results show that for various conditions of post-encroachment time, the ego vehicle adopting the proposed strategy will conduct reasonable behavior re-planning and consequently successfully avoid obstacles.

Road Feel Design for Vehicle Steer-by-Wire System Based on Joystick

2013 ◽  
Vol 336-338 ◽  
pp. 734-737
Author(s):  
Hong Yu Zheng ◽  
Ya Ning Han ◽  
Chang Fu Zong
Keyword(s):  
Computer Simulation ◽  
Control Strategy ◽  
Vehicle Speed ◽  
Control Module ◽  
Matlab Simulink ◽  
The Road ◽  
Steering Feel ◽  
Steer By Wire ◽  
Simulation Results ◽  
Wire System

In order to solve the problem of road feel feedback of vehicle steer-by-wire (SBW) system based on joystick, a road feel control strategy was established to analyze the road feel theory of traditional steer system, which included return, assist and damp control module. By verifying the computer simulation results with the control strategy from software of CarSim and Matlab/Simulink, it shows that the proposed strategy can effective get road feel in different vehicle speed conditions and could improve the vehicle maneuverability to achieve desired steering feel by different drivers.

Study of Rider Model for Motorcycle Racing Simulation

2020 ◽  
Author(s):  
Masatsugu Nishimura ◽  
Yoshitaka Tezuka ◽  
Enrico Picotti ◽  
Mattia Bruschetta ◽  
Francesco Ambrogi ◽  
...  
Keyword(s):  
Predictive Control ◽  
Running Speed ◽  
Steering Angle ◽  
Conventional Model ◽  
Linear Region ◽  
New Model ◽  
Constant Radius ◽  
Simulation Results ◽  
Braking Force ◽  
Motorcycle Dynamics

Various rider models have been proposed that provide control inputs for the simulation of motorcycle dynamics. However, those models are mostly used to simulate production motorcycles, so they assume that all motions are in the linear region such as those in a constant radius turn. As such, their performance is insufficient for simulating racing motorcycles that experience quick acceleration and braking. Therefore, this study proposes a new rider model for racing simulation that incorporates Nonlinear Model Predictive Control. In developing this model, it was built on the premise that it can cope with running conditions that lose contact with the front wheels or rear wheels so-called "endo" and "wheelie", which often occur during running with large acceleration or deceleration assuming a race. For the control inputs to the vehicle, we incorporated the lateral shift of the rider's center of gravity in addition to the normally used inputs such as the steering angle, throttle position, and braking force. We compared the performance of the new model with that of the conventional model under constant radius cornering and straight braking, as well as complex braking and acceleration in a single (hairpin) corner that represented a racing run. The results showed that the new rider model outperformed the conventional model, especially in the wider range of running speed usable for a simulation. In addition, we compared the simulation results for complex braking and acceleration in a single hairpin corner produced by the new model with data from an actual race and verified that the new model was able to accurately simulate the run of actual MotoGP riders.

Coordinated Model Predictive Control on Multi-Lane Roads

2019 ◽  
Author(s):  
Nathan Goulet ◽  
Beshah Ayalew
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Heterogeneous Traffic ◽  
Road Transportation ◽  
The Road ◽  
Energy Saving Potential ◽  
Coordination Scheme ◽  
Predictive Controller

Abstract There are significant economic, environmental, energy, and other societal costs incurred by the road transportation sector. With the advent and penetration of connected and autonomous vehicles there are vast opportunities to optimize the control of individual vehicles for reducing energy consumption and increasing traffic flow. Model predictive control is a useful tool to achieve such goals, while accommodating ego-centric objectives typical of heterogeneous traffic and explicitly enforcing collision and other constraints. In this paper, we describe a multi-agent distributed maneuver planning and lane selection model predictive controller that includes an information sharing and coordination scheme. The energy saving potential of the proposed coordination scheme is then evaluated via large scale microscopic traffic simulations considering different penetration levels of connected and automated vehicles.

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