scholarly journals Smart Longitudinal Velocity Control of Autonomous Vehicles in Interactions With Distracted Human-Driven Vehicles

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 168060-168074 ◽  
Author(s):  
Wen Yan ◽  
Chunguo Li ◽  
Yongming Huang ◽  
Luxi Yang
Keyword(s):  
Autonomous Vehicles ◽  
Longitudinal Velocity ◽  
Velocity Control

Yaw rate tracking-based path-following control for four-wheel independent driving and four-wheel independent steering autonomous vehicles considering the coordination with dynamics stability

2020 ◽  
Vol 235 (1) ◽  
pp. 260-272
Author(s):  
Yixiao Liang ◽  
Yinong Li ◽  
Ling Zheng ◽  
Yinghong Yu ◽  
Yue Ren
Keyword(s):  
Autonomous Vehicles ◽  
Longitudinal Velocity ◽  
Path Following ◽  
Linear Matrix ◽  
Yaw Rate ◽  
Path Following Control ◽  
Inequality Theory ◽  
Conflicting Objectives ◽  
Upper Level ◽  
Dynamics Stability

The path-following problem for four-wheel independent driving and four-wheel independent steering electric autonomous vehicles is investigated in this paper. Owing to the over-actuated characters of four-wheel independent driving and four-wheel independent steering autonomous vehicles, a novel yaw rate tracking-based path-following controller is proposed. First, according to the kinematic relationships between vehicle and the reference path, the yaw rate generator is designed by linear matrix inequality theory, with the ability to minimize the disturbances caused by vehicle side slip and varying curvature of path. Considering that the path-following objective and dynamics stability are in conflict with each other in some extreme path-following conditions, a coordinating mechanism based on yaw rate prediction is proposed to satisfy the two conflicting objectives. Then, according to the desired yaw rate and longitudinal velocity, a hierarchical structure is introduced for motion control. The upper-level controller calculates the generalized tracking forces while the allocation layer optimally distributes the generalized forces to tires considering tire vertical load and adhesive utilization. Finally, simulation results indicate that the proposed method can achieve excellent path-following performances in different driving conditions, while both path-following objective and dynamics stability can be satisfied.

scholarly journals Vehicle Velocity Estimation Fusion with Kinematic Integral and Empirical Correction on Multi-Timescales

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1242
Author(s):  
Jiangyi Lv ◽  
Hongwen He ◽  
Wei Liu ◽  
Yong Chen ◽  
Fengchun Sun
Keyword(s):  
Autonomous Vehicles ◽  
Estimation Error ◽  
Longitudinal Velocity ◽  
Estimation Method ◽  
Velocity Estimation ◽  
Model Parameters ◽  
Lateral Velocity ◽  
Empirical Correction ◽  
Vehicle Velocity ◽  
Wide Range

Accurate and reliable vehicle velocity estimation is greatly motivated by the increasing demands of high-precision motion control for autonomous vehicles and the decreasing cost of the required multi-axis IMU sensors. A practical estimation method for the longitudinal and lateral velocities of electric vehicles is proposed. Two reliable driving empirical judgements about the velocities are extracted from the signals of the ordinary onboard vehicle sensors, which correct the integral errors of the corresponding kinematic equations on a long timescale. Meanwhile, the additive biases of the measured accelerations are estimated recursively by comparing the integral of the measured accelerations with the difference of the estimated velocities between the adjacent strong empirical correction instants, which further compensates the kinematic integral error on short timescale. The algorithm is verified by both the CarSim-Simulink co-simulation and the controller-in-the-loop test under the CarMaker-RoadBox environment. The results show that the velocities can be accurately and reliably estimated under a wide range of driving conditions without prior knowledge of the tire-model and other unavailable signals or frequently changeable model parameters. The relative estimation error of the longitudinal velocity and the absolute estimation error of the lateral velocity are kept within 2% and 0.5 km/h, respectively.

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.

scholarly journals Integrated Avoid Collision Control of Autonomous Vehicle Based on Trajectory Re-Planning and V2V Information Interaction

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1079 ◽  
Author(s):  
Fen Lin ◽  
Kaizheng Wang ◽  
Youqun Zhao ◽  
Shaobo Wang
Keyword(s):  
Autonomous Vehicles ◽  
Control Method ◽  
Integrated Control ◽  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Reference Trajectory ◽  
Threshold Method ◽  
Lane Changing ◽  
Information Interaction ◽  
Fitting In

An integrated longitudinal-lateral control method is proposed for autonomous vehicle trajectory tracking and dynamic collision avoidance. A method of obstacle trajectory prediction is proposed, in which the trajectory of the obstacle is predicted and the dynamic solution of the reference trajectory is realized. Aiming at the lane changing scene of autonomous vehicles driving in the same direction and adjacent lanes, a trajectory re-planning motion controller with the penalty function is designed. The reference trajectory parameterized output of local reprogramming is realized by using the method of curve fitting. In the framework of integrated control, Fuzzy adaptive (proportional-integral) PI controller is proposed for longitudinal velocity tracking. The selection and control of controller and velocity are realized by logical threshold method; A model predictive control (MPC) with vehicle-to-vehicle (V2V) information interaction modular and the driver characteristics is proposed for direction control. According to the control target, the objective function and constraints of the controller are designed. The proposed method’s performance in different scenarios is verified by simulation. The results show that the autonomous vehicles can avoid collision and have good stability.

A Single Forward-Velocity Control Signal for Stochastic Source Seeking With Multiple Nonholonomic Vehicles

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Paul Frihauf ◽  
Shu-Jun Liu ◽  
Miroslav Krstic
Keyword(s):  
Autonomous Vehicles ◽  
Small Neighborhood ◽  
Control Signal ◽  
Extremum Seeking ◽  
Velocity Control ◽  
Extremum Seeking Control ◽  
Spatially Distributed ◽  
Angular Velocities ◽  
Nonholonomic Vehicles ◽  
Signal Field

With a single stochastic extremum seeking control signal, we steer multiple autonomous vehicles, modeled as nonholonomic unicycles, toward the maximum of an unknown, spatially distributed signal field. The vehicles, whose angular velocities are constant and distinct, travel at the same forward velocity, which is controlled by the stochastic extremum seeking controller. To determine the vehicles’ velocity, the controller uses measurements of the signal field at the respective vehicle positions and excitation based on filtered white noise. The positions of the vehicles are not measured. We prove local exponential convergence, both almost surely and in probability, to a small neighborhood near the source and provide a numerical example to illustrate the effectiveness of the algorithm.

Longitudinal Velocity Control Design with Error Tolerance Strategy for Autonomous Vehicle

2019 ◽  
pp. 1-11
Author(s):  
Umar Zakir Abdul Hamid ◽  
Balaji Ravichandiran ◽  
Murtadha Bazli Tukimat ◽  
Hairi Zamzuri ◽  
Fakhrul Razi Ahmad Zakuan ◽  
...  
Keyword(s):  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Control Design ◽  
Error Tolerance ◽  
Velocity Control ◽  
Tolerance Strategy
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