A locomotion control method for autonomous vehicles

2003 ◽  
Author(s):  
Y. Kanayama ◽  
A. Nilipour ◽  
C.A. Lelm
Keyword(s):  
Autonomous Vehicles ◽  
Control Method ◽  
Locomotion Control

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

scholarly journals System Modeling and Simulation of an Unmanned Aerial Underwater Vehicle

2019 ◽  
Vol 7 (12) ◽  
pp. 444 ◽  
Author(s):  
Yuqing Chen ◽  
Yaowen Liu ◽  
Yangrui Meng ◽  
Shuanghe Yu ◽  
Yan Zhuang
Keyword(s):  
Autonomous Vehicles ◽  
Water Resistance ◽  
Control Method ◽  
Sliding Mode ◽  
System Modeling ◽  
Environmental Disturbances ◽  
Model Method ◽  
Attitude Error ◽  
The Cross ◽  
Single Set

Unmanned Aerial Underwater Vehicles (UAUVs) with multiple propellers can operate in two distinct mediums, air and underwater, and the system modeling of the autonomous vehicles is a key issue to adapt to these different external environments. In this paper, only a single set of aerial rotors with switching propulsion abilities are designed as driving components, and then a compound multi-model method is investigated to achieve good performance of the cross-medium motion. Furthermore, some additional variables, such as water resistance, buoyancy and their corresponding moments are considered for the underwater case. In particular, a critical coefficient for air-to-water switching is presented to express these gradually changing additional variables in the cross-medium motion process. Finally, the sliding mode control method is used to reduce the altitude error and attitude error of the vehicles with external environmental disturbances. The proposed scheme is tested and the model is verified on the simulation platform.

scholarly journals A Nonlinear H-infinity Control Approach for Autonomous Truck and Trailer Systems

2020 ◽  
Vol 08 (01) ◽  
pp. 49-69 ◽  
Author(s):  
Gerasimos Rigatos ◽  
Pierluigi Siano ◽  
Patrice Wira ◽  
Krishna Busawon ◽  
Richard Binns
Keyword(s):  
Autonomous Vehicles ◽  
Control Method ◽  
Kinematic Model ◽  
Taylor Series Expansion ◽  
Time Instant ◽  
Algebraic Riccati Equation ◽  
Control Loop ◽  
H Infinity ◽  
Control Approach ◽  
H Infinity Control

A nonlinear optimal control method is developed for autonomous truck and trailer systems. Actually, two cases are distinguished: (a) a truck and trailer system that is steered by the front wheels of its truck, (b) an autonomous fire-truck robot that is steered by both the front wheels of its truck and by the rear wheels of its trailer. The kinematic model of the autonomous vehicles undergoes linearization through Taylor series expansion. The linearization is computed at a temporary operating point that is defined at each time instant by the present value of the state vector and the last value of the control inputs vector. The linearization is based on the computation of Jacobian matrices. The modeling error due to approximate linearization is considered to be a perturbation that is compensated by the robustness of the control scheme. For the approximately linearized model of the autonomous vehicles an H-infinity feedback controller is designed. This requires the solution of an algebraic Riccati equation at each iteration of the control algorithm. The stability of the control loop is confirmed through Lyapunov analysis. It is shown that the control loop exhibits the H-infinity tracking performance which implies elevated robustness against modeling errors and external disturbances. Moreover, under moderate conditions the global asymptotic stability of the control loop is proven. Finally, to implement state estimation-based control for the autonomous vehicles, through the processing of a small number of sensor measurements, the H-infinity Kalman Filter is proposed.

scholarly journals Development of a conflict-free unsignalized intersection organization method for multiple connected and autonomous vehicles

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249170
Author(s):  
Qinglu Ma ◽  
Shu Zhang ◽  
Qi Zhou
Keyword(s):  
Traffic Control ◽  
Autonomous Vehicles ◽  
Control Method ◽  
Operating Efficiency ◽  
Signal Control ◽  
Vehicle Speed ◽  
Operating Characteristics ◽  
Right Of Way ◽  
Unsignalized Intersections ◽  
Traffic Organization

An effective traffic control strategy will improve travel reliability in urban transportation networks. Lack of coordination between vehicles, however, exacerbates congestion due mainly to frequent stops at unsignalized intersections. It is beneficial to develop a conflict-free cooperation method that collects basic safety message from multiple approaching Connected and Autonomous Vehicles (for short, CAVs) and guarantees efficient unsignalized intersection operations with safe and incident free vehicle maneuvers. This paper proposes an interspersed traffic organization method under controlled constraints. Firstly, relied on shared location technology and considered the operating characteristics of CAVs at unsignalized intersections to detect and analyze traffic conflicts to establish a right-of-way judgment model for CAVs. In order to further ensure the safety and operating efficiency of the vehicle, based on the judgment results of right-of-way judgment model, a vehicle speed guidance model is established for different traffic conditions. Taking the real city standard intersection as the experimental analysis object, through data collection and simulation experiment, the signal control method and the organization method proposed in this paper are compared and analyzed. The results showed that the traffic organization method proposed in this paper improves the operational efficiency of 46%, the average travel time is reduced by 6.54s, which is not only better than the signal control method, but also supports the development of car networking technology.

Comparative Analysis of MacAdam Model and PI Control in a Predictive Driver Model

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
C. Dias ◽  
J. Landre ◽  
P. Americo ◽  
M. Campolina ◽  
L. Marino Marino ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Degrees Of Freedom ◽  
Control Method ◽  
Longitudinal Velocity ◽  
Pi Controller ◽  
Driver Model ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Reference Path ◽  
Double Lane Change

Autonomous vehicles are the future of automotive engineering and understanding how this systems work is critical. In these vehicles, controller models are usually needed to generate signals that would normally be imposed by the driver e.g., steering angles, acceleration inputs and braking commands. Intuitively, each control method utilized has its peculiarities and presents different behaviours. In such situation, this paper aims to develop an error comparison between a car displacement and its reference path due the use of two different predictive driver controllers: The proportional-integrative and the MacAdam model. For this purpose, a 14 degrees of freedom vehicle model is used with the aid of MATLAB Simulink, whereas simulations were made using the double-lane change manoeuvre, a commonly used manoeuvre to analyse the vehicle dynamics performance. At the end of this paper, lateral acceleration, displacement and steering wheel angle analysis led the conclusion that the vehicle behaviour is smoother with the use of the proportional-integrative control regardless of longitudinal velocity. Nevertheless, the trajectory error is smaller for MacAdam model than PI controller is and therefore it is easier to follow the reference path with this one, although in aggressive maneuverers it can cause more discomfort and increase the risk of rolling when compared to the PI controller in a vehicle with the same body stiffness.

Prediction of Traffic Signal and Longitudinal Control Method for Autonomous Vehicles using V2X

2020 ◽  
Vol 30 (3) ◽  
pp. 173-180
Author(s):  
Suhyun Yoon ◽  
Hae Jun Jo ◽  
Seong Woo Kwak ◽  
Jae Cheon Lee ◽  
Ho Seung Lee
Keyword(s):  
Autonomous Vehicles ◽  
Control Method ◽  
Traffic Signal ◽  
Longitudinal Control
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