A novel path tracking approach considering safety of the intended functionality for autonomous vehicles

2021 ◽  
pp. 095440702110205
Author(s):  
Huiran Wang ◽  
Qidong Wang ◽  
Wuwei Chen ◽  
Linfeng Zhao ◽  
Dongkui Tan
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Coordinated Control ◽  
Steering System ◽  
Front Wheel ◽  
Path Tracking ◽  
Hierarchical Architecture ◽  
Automatic Steering ◽  
The Stability ◽  
Control Layer

To reduce the adverse effect of the functional insufficiency of the steering system on the accuracy of path tracking, a path tracking approach considering safety of the intended functionality is proposed by coordinating automatic steering and differential braking in this paper. The proposed method adopts a hierarchical architecture consisting of a coordinated control layer and an execution control layer. In coordinated control layer, an extension controller considering functional insufficiency of the steering system, tire force characteristics and vehicle driving stability is proposed to determine the weight coefficients of automatic steering and the differential braking, and a model predictive controller is designed to calculate the desired front wheel angle and additional yaw moment. In execution control layer, a H∞ steering angle controller considering external disturbances and parameter uncertainty is designed to track desired front wheel angle, and a braking force distribution module is used to determine the wheel cylinder pressure of the controlled wheels. Both simulation and experiment results show that the proposed method can overcome the functional insufficiency of the steering system and improve the accuracy of path tracking while maintaining the stability of the autonomous vehicle.

scholarly journals Research on Curved Path Tracking Control for Four-Wheel Steering Vehicle considering Road Adhesion Coefficient

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Runqiao Liu ◽  
Minxiang Wei ◽  
Nan Sang ◽  
Jianwei Wei
Keyword(s):  
Autonomous Vehicles ◽  
Tracking Control ◽  
Spline Interpolation ◽  
Lateral Displacement ◽  
Autonomous Vehicle ◽  
State Parameter ◽  
Front Wheel ◽  
Path Tracking ◽  
Adhesion Coefficient ◽  
Polar Coordinate

Curved path tracking control is one of the most important functions of autonomous vehicles. First, small turning radius circular bends considering bend quadrant and travel direction restrictions are planned by polar coordinate equations. Second, an estimator of a vehicle state parameter and road adhesion coefficient based on an extended Kalman filter is designed. To improve the convenience and accuracy of the estimator, the combined slip theory, trigonometric function group fitting, and cubic spline interpolation are used to estimate the longitudinal and lateral forces of the tire model (215/55 R17). Third, to minimize the lateral displacement and yaw angle tracking errors of a four-wheel steering (4WS) vehicle, the front-wheel steering angle of the 4WS vehicle is corrected by a model predictive control (MPC) feed-back controller. Finally, CarSim® simulation results show that the 4WS autonomous vehicle based on the MPC feed-back controller can not only significantly improve the curved path tracking performance but also effectively reduce the probability of drifting or rushing out of the runway at high speeds and on low-adhesion roads.

scholarly journals Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3425
Author(s):  
Huanping Li ◽  
Jian Wang ◽  
Guopeng Bai ◽  
Xiaowei Hu
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Flow Model ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Transformation Model ◽  
Hydrodynamic Theory ◽  
Traffic Flow Model ◽  
Traffic System ◽  
The Stability

In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbitrary proportion (p) of autonomous vehicles. Using calculus and difference methods, a speed transformation model is established which could make the autonomous/human-driven vehicles maintain synchronized speed changes. Based on multi-hydrodynamic theory, a mixed traffic flow model capable of numerical calculation is established to predict the changes in traffic flow under different proportions of autonomous vehicles, then obtain the redistribution characteristics of traffic flow. Results show that the reaction time of autonomous vehicles has a decisive influence on traffic capacity; the q-k curve for mixed human/autonomous traffic remains in the region between the q-k curves for 100% human and 100% autonomous traffic; the participation of autonomous vehicles won’t bring essential changes to road traffic parameters; the speed-following transformation model minimizes the safety distance and provides a reference for the bottom program design of autonomous vehicles. In general, the research could not only optimize the stability of transportation system operation but also save road resources.

scholarly journals Deep reinforcement learning based control for Autonomous Vehicles in CARLA

2022 ◽  
Author(s):  
Óscar Pérez-Gil ◽  
Rafael Barea ◽  
Elena López-Guillén ◽  
Luis M. Bergasa ◽  
Carlos Gómez-Huélamo ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Data Sources ◽  
Simulation Environment ◽  
Urban Simulation ◽  
Policy Gradient ◽  
Almost All ◽  
Control Layer

AbstractNowadays, Artificial Intelligence (AI) is growing by leaps and bounds in almost all fields of technology, and Autonomous Vehicles (AV) research is one more of them. This paper proposes the using of algorithms based on Deep Learning (DL) in the control layer of an autonomous vehicle. More specifically, Deep Reinforcement Learning (DRL) algorithms such as Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) are implemented in order to compare results between them. The aim of this work is to obtain a trained model, applying a DRL algorithm, able of sending control commands to the vehicle to navigate properly and efficiently following a determined route. In addition, for each of the algorithms, several agents are presented as a solution, so that each of these agents uses different data sources to achieve the vehicle control commands. For this purpose, an open-source simulator such as CARLA is used, providing to the system with the ability to perform a multitude of tests without any risk into an hyper-realistic urban simulation environment, something that is unthinkable in the real world. The results obtained show that both DQN and DDPG reach the goal, but DDPG obtains a better performance. DDPG perfoms trajectories very similar to classic controller as LQR. In both cases RMSE is lower than 0.1m following trajectories with a range 180-700m. To conclude, some conclusions and future works are commented.

Stability of Controlled Road Vehicles: A Preliminary Fundamental Study

2015 ◽  
Author(s):  
Fabio della Rossa ◽  
Massimiliano Gobbi ◽  
Giampiero Mastinu ◽  
Carlo Piccardi ◽  
Giorgio Previati
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Preliminary Investigation ◽  
Basin Of Attraction ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Vehicle Model ◽  
The Stability ◽  
Positive Effect

A comparison of the lateral stability behaviour between an autonomous vehicle, a vehicle with driver and a vehicle without driver (fixed steering wheel) is made by introducing a simple mathematical model of a vehicle running on even road. The mechanical model of the vehicle has two degrees of freedom and the related equations of motion contain the nonlinear tyre characteristics. The driver is described by a well-known model proposed in the literature. The autonomous vehicle has a virtual driver (robot) that behaves substantially like a human, but with its proper reaction time and gain. The road vehicle model has been validated. The study of vehicle stability has to be based on bifurcation analysis and a preliminary investigation is proposed here. The accurate computation of steady-state equilibria is crucial to study the stability of the three kinds of vehicles here compared. The stability of the bare vehicle without driver (fixed steering wheel) is studied in a rather complete way referring to a number of combinations of tyre characteristics. The (known) conclusion is that the understeering vehicle is stable at each lateral acceleration level and at each vehicle speed. The additional (partially unknown) conclusion is that the vehicle (model) with degradated tyres may exhibit a huge number of different bifurcations. The driver has many effects on the stability of the vehicle. One positive effect is to eliminate the many possible different equilibria of the bare vehicle and keep active one single equilibrium only. Another positive effect is to broaden the basin of attraction of stable equilibria (at least at relatively low speed). A negative effect is that, even for straight running, the driver seem introducing a subcritical Hopf bifurcation which limits the maximum forward speed of some understeering vehicles (that could run faster with fixed steering wheel). Both the mentioned positive and negative effects appear to be applicable to autonomous vehicles as well. Further studies could be useful to overcome the limitations on the stability of current autonomous vehicles that have been identified in the present research.

scholarly journals EQUATION OF MOTION OF THE JOHN DEERE 6095B TRACTOR AS AN AUTOMATIC DRIVING OBJECT

2020 ◽  
pp. 44-55
Author(s):  
Evgeniy Kalinin ◽  
Olexander Saychuk ◽  
Nadiia Kolpachenko
Keyword(s):  
Agricultural Production ◽  
Equations Of Motion ◽  
Steering System ◽  
Front Wheel ◽  
Point Of View ◽  
Centrifugal Forces ◽  
Automatic Driving ◽  
Current State ◽  
Automatic Steering ◽  
The Right

The main issue that has to be solved in the formation of modern systems for automatic driving of tractors is the issue of obtaining information about the current state of the machine-tractor unit relative to a given trajectory. In terms of its quality, this information should reflect the rather stringent requirements of agricultural production for the accuracy of trajectory control. Thus, to create devices for automatic driving of tractors, it is necessary to know the characteristics and properties of these machines as objects from the point of view of the theory of automatic control. When examining such machines, first of all, it is necessary to establish which parameter should be considered as input. With manual control, the feedback is closed visually on the right front wheel, more precisely at the point of contact of the wheel with the ground. The main goal of this work is to obtain equations connecting the input and output coordinates, as well as the input coordinate and coordinates of the middle of the front and rear axles of the tractor. It is also necessary to establish under what initial data a simplified equation can be used, taking into account only the kinematics of the tractor movement and not taking into account the elasticity of tires and deformation of the soil. For this, the problem is solved both taking into account the elasticity of tires and deformation of the soil, and without taking into account these factors. Frequency characteristics are compared, obtained using a simplified equation and taking into account the above factors at different speeds. During the research, the equations of motion of the tractor were obtained taking into account the deformation of pneumatic tires and soil. This equation allows you to study the movement of the tractor in the presence of external lateral forces. Such forces can be centrifugal forces when moving along a curved trajectory and forces from trailed and mounted implements on a tractor. The equation is valid for small steering angles of the tractor idler wheels. A simplified equation is obtained that does not take into account the deformation of tires and soil. This equation can roughly describe the movement of a tractor on solid ground, which is little deformed, at relatively low speeds. It is advisable to use this equation only at speeds not exceeding 1.7 m/s on dense ground. Both equations characterize the tractor as an object of regulation and allow the selection and design of an automatic steering system.

scholarly journals Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

2022 ◽  
Vol 35 (1) ◽  
Author(s):  
Ying Tian ◽  
Qiangqiang Yao ◽  
Peng Hang ◽  
Shengyuan Wang
Keyword(s):  
Control Strategy ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
High Speed ◽  
Front Wheel ◽  
Path Tracking ◽  
Vehicle Stability ◽  
Tracking Accuracy ◽  
Large Curvature ◽  
Yaw Moment

AbstractIt is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions. In this study, an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm. The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model. To adaptively adjust the priorities of path tracking accuracy and vehicle stability, an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function. An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions. To ensure vehicle stability, the sideslip angle, yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame. It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and large-curvature conditions.

scholarly journals Identification of an optimum control algorithm to reject unwanted yaw effect on wheeled armored vehicle due to the recoil force

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668335 ◽  
Author(s):  
Vimal R Aparow ◽  
Khisbullah Hudha ◽  
Zulkiffli A Kadir ◽  
Noor H Amer ◽  
Shohaimi Abdullah ◽  
...  
Keyword(s):  
Dynamic Condition ◽  
Control Strategies ◽  
External Disturbance ◽  
Steering System ◽  
Front Wheel ◽  
Optimum Control ◽  
Active Safety System ◽  
Armored Vehicle ◽  
The Stability ◽  
Yaw Moment

This article presents an active safety system for a wheeled armored vehicle to encounter the effect of the firing force. The firing force which acts as an external disturbance causes unwanted yaw moment occurred at the center of gravity of the wheeled armored vehicle. This effect causes the wheeled armored vehicle lose its handling stability and the traveling path after the firing condition. In order to overcome the stability problem, a Firing-On-the-Move assisted by an Active Front Wheel Steering system is proposed in this study. This system is developed based on two established systems, namely, Firing-On-the-Move and Active Front Wheel Steering systems. The proposed system is designed to improve the handling and directional stability performances of the armored vehicle while fires in dynamic condition. Four types of control strategies are designed and investigated in this study to identify the most optimum control strategy as the Firing-On-the-Move assisted by an Active Front Wheel Steering system using optimization tool, genetic algorithm. The control strategies for the Firing-On-the-Move assisted by an Active Front Wheel Steering are evaluated using various types of vehicle speeds and firing angle in order to obtain an appropriate control structure as the Firing-On-the-Move assisted by an Active Front Wheel Steering system for the wheeled armored vehicle.

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