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.

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 A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

Understanding the Lateral Dimension of Traffic: Measuring and Modeling Lane Discipline

2021 ◽  
pp. 036119812110318
Author(s):  
Rafael Delpiano
Keyword(s):  
New Technologies ◽  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Lateral Position ◽  
Multiple Cameras ◽  
Lateral Velocity ◽  
Lane Changing ◽  
Lateral Dimension ◽  
Vehicle Position ◽  
Longitudinal Position

There is growing interest in understanding the lateral dimension of traffic. This trend has been motivated by the detection of phenomena unexplained by traditional models and the emergence of new technologies. Previous attempts to address this dimension have focused on lane-changing and non-lane-based traffic. The literature on vehicles keeping their lanes has generally been limited to simple statistics on vehicle position while models assume vehicles stay perfectly centered. Previously the author developed a two-dimensional traffic model aiming to capture such behavior qualitatively. Still pending is a deeper, more accurate comprehension and modeling of the relationships between variables in both axes. The present paper is based on the Next Generation SIMulation (NGSIM) datasets. It was found that lateral position is highly dependent on the longitudinal position, a phenomenon consistent with data capture from multiple cameras. A methodology is proposed to alleviate this problem. It was also discovered that the standard deviation of lateral velocity grows with longitudinal velocity and that the average lateral position varies with longitudinal velocity by up to 8 cm, possibly reflecting greater caution in overtaking. Random walk models were proposed and calibrated to reproduce some of the characteristics measured. It was determined that drivers’ response is much more sensitive to the lateral velocity than to position. These results provide a basis for further advances in understanding the lateral dimension. It is hoped that such comprehension will facilitate the design of autonomous vehicle algorithms that are friendlier to both passengers and the occupants of surrounding vehicles.

Trajectory Tracking of 3D Autonomous Vehicles Using Backstepping Control

2019 ◽  
Author(s):  
Karl Ludwig Fetzer ◽  
Sergey G. Nersesov ◽  
Hashem Ashrafiuon
Keyword(s):  
Autonomous Vehicles ◽  
Degrees Of Freedom ◽  
3D Model ◽  
Autonomous Vehicle ◽  
Backstepping Control ◽  
Reference Trajectory ◽  
Control Laws ◽  
Six Degrees Of Freedom ◽  
Error Dynamics ◽  
Dynamics Stability

Abstract In this paper, the authors derive backstepping control laws for tracking a time-based reference trajectory for a 3D model of an autonomous vehicle with two degrees of underactuation. Tracking all six degrees of freedom is made possible by a transformation that reduces the order of the error dynamics. Stability of the resulting error dynamics is proven and demonstrated in simulations.

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.

Social Force Control for Human-Like Autonomous Driving

2018 ◽  
Author(s):  
DoHyun Daniel Yoon ◽  
Beshah Ayalew
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Social Force ◽  
Cruise Control ◽  
Lane Changing ◽  
Safe Navigation ◽  
Proposed Model ◽  
The Social ◽  
Efficient Integration

An autonomous driving control system that incorporates notions from human-like social driving could facilitate an efficient integration of hybrid traffic where fully autonomous vehicles (AVs) and human operated vehicles (HOVs) are expected to coexist. This paper aims to develop such an autonomous vehicle control model using the social-force concepts, which was originally formulated for modeling the motion of pedestrians in crowds. In this paper, the social force concept is adapted to vehicular traffic where constituent navigation forces are defined as a target force, object forces, and lane forces. Then, nonlinear model predictive control (NMPC) scheme is formulated to mimic the predictive planning behavior of social human drivers where they are considered to optimize the total social force they perceive. The performance of the proposed social force-based autonomous driving control scheme is demonstrated via simulations of an ego-vehicle in multi-lane road scenarios. From adaptive cruise control (ACC) to smooth lane-changing behaviors, the proposed model provided a flexible yet efficient driving control enabling a safe navigation in various situations while maintaining reasonable vehicle dynamics.

scholarly journals Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections

2020 ◽  
Vol 10 (20) ◽  
pp. 7161
Author(s):  
András Mihály ◽  
Zsófia Farkas ◽  
Péter Gáspár
Keyword(s):  
Autonomous Vehicles ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Signalized Intersections ◽  
Complex Simulation ◽  
Control Goal ◽  
Predictive Controller ◽  
Multicriteria Model ◽  
Time And Energy ◽  
Centralized Controller

The aim of the paper is to describe a multicriteria model predictive control method for autonomous vehicles at non-signalized intersections. The centralized controller aims to describe control action for each autonomous vehicle to guarantee collision free passage. At the same time, performances are defined for the centralized Model Predictive Controller, namely the minimization of traveling time and energy consumption. Since these control goals are often conflicting, a scheduling variable is introduced to create a balance between them. Hence, the centralized controller can be tuned based on the importance of each control goal, which can be useful in urban environment where traffic densities may vary heavily depending on the period of the day. The effectiveness of the proposed centralized multicriteria controller is demonstrated through a complex simulation example in CarSim simulation environment using different tpye of autonomous vehicles.

scholarly journals Simulation Study of Autonomous Vehicles’ Effect on Traffic Flow Characteristics including Autonomous Buses

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Tanveer Muhammad ◽  
Faizan Ahmad Kashmiri ◽  
Hassan Naeem ◽  
Xin Qi ◽  
Hsu Chia-Chun ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Flow Rate ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Flow Characteristics ◽  
Transportation Industry ◽  
Lane Changing ◽  
Autonomous Cars ◽  
Ca Model ◽  
Multiple Scenarios

Autonomous vehicles are expected to revolutionize the transportation industry. The goal of this research is to study the heterogeneity in traffic flow dynamics by comparing different penetration rates of four different types of vehicles: autonomous cars (AC), autonomous buses (AB), manual cars (MC), and manual buses (MB). For the purpose of this research, a modified cellular automata (CA) model is developed in order to analyze the effect of heterogeneous vehicles (manual and autonomous). Previously, studies have focused on manual and autonomous cars, but we believe a gap in perception and analysis of mixed traffic still exists, as inclusion of other modes of autonomous vehicle research is very limited. Therefore, we have explicitly examined the effect of the AB on overall traffic flow. Moreover, two types of lane changing behavior (aggressive lane changing and polite lane changing) were also integrated into the model. Multiple scenarios through different compositions of vehicles were simulated. As per the results, if AB is employed concurrently with AC, there will be a significant improvement in traffic flow and road capacity, as equally more passengers can be accommodated in AB as AC is also anticipated to be used in carpooling. Secondly, when the vehicles change the lanes aggressively, there is a substantial growth in the flow rate and capacity of the network. Polite lane change does not significantly affect the flow rate.

Multi-Player Dynamic Game-Based Automatic Lane-Changing Decision Model under Mixed Autonomous Vehicle and Human-Driven Vehicle Environment

2020 ◽  
Vol 2674 (11) ◽  
pp. 165-183
Author(s):  
Yuewen Yu ◽  
Shikun Liu ◽  
Peter J. Jin ◽  
Xia Luo ◽  
Mengxue Wang
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Splitting Method ◽  
Dynamic Game ◽  
Cruise Control ◽  
Lane Changing ◽  
Bicycle Model ◽  
Decision Making Model

The lane-changing decision-making process is challenging but critical to ensure safe and smooth maneuvers for autonomous vehicles (AVs). Conventional Gipps-type algorithms lack the flexibility for practical use under a mixed autonomous vehicle and human-driven vehicle (AV-HV) environment. Algorithms based on utility ignore the reactions of surrounding vehicles to the lane-changing vehicle. Game theory is a good way to solve the shortcomings of current algorithms, but most models based on game theory simplify the game with surrounding vehicles to the game with the following vehicle in the target lane, which means that the lane-changing decision under a mixed environment is not realized. This paper proposes a lane-changing decision-making model which is suitable for an AV to change lanes under a mixed environment based on a multi-player dynamic game theory. The overtaking expectation parameter (OEP) is introduced to estimate the utility of the following vehicle, OEP can be calculated by the proposed non-lane-based full velocity difference model with the consideration of lateral move and aggressiveness. This paper further proposes a hybrid splitting method algorithm to obtain the Nash equilibrium solution in the multi-player game to obtain the optimal strategy of lane-changing decision for AVs. An adaptive cruise control simulation environment is developed with MATLAB’s Simulink toolbox using Next Generation Simulation (NGSIM) data as the background traffic flow. The classic bicycle model is used in the control of involved HVs. Simulation results show the efficiency of the proposed multi-player dynamic game-based algorithm for lane-changing decision making by AVs under a mixed AV-HV environment.

scholarly journals Integrated Longitudinal and Lateral Control System Design and Case Study on an Electric Vehicle

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Jin Zhao ◽  
Haolong Fu ◽  
Dongjie Liu ◽  
Guangwei Wang ◽  
Abdelkader El Kamel
Keyword(s):  
Electric Vehicle ◽  
Longitudinal Design ◽  
Integrated Control ◽  
Autonomous Vehicle ◽  
Field Tests ◽  
Lane Changing ◽  
Experiment Platform ◽  
Lane Tracking ◽  
Upper Level

This paper presents the design of an integrated longitudinal and lateral controller for autonomous vehicle and field tests with an electric vehicle. First, the longitudinal design was studied which includes the spacing policy as the upper level controller and throttle and brake control as the lower level controller. A safety spacing policy was proposed considering both the vehicle states and the vehicle capability. A coordinated throttle and brake controller was also designed to ensure the vehicle pursuing the desired acceleration. Second, a multimodel lateral controller was proposed which can perform the lane tracking and lane changing manoeuvres. Then, an integrated control structure was proposed to manage both the longitudinal and lateral controller. Finally, simulation and visualization works were carried out to validate the proposed solutions. An electric vehicle experiment platform was also built, and field tests showed encouraging results.

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