Centralized model predictive control of autonomous driving vehicles with Lyapunov stability

Rafaila, Razvan C.; Caruntu, Constantin F.; Livint, Gheorghe

doi:10.1109/icstcc.2016.7790742

“…Weiskircher et al [35] proposed a predictive trajectory guidance and control framework that enables the safe operation of autonomous and semiautonomous vehicles considering the constraints of operating in dynamic public traffic. Rafaila et al [36] and Koga et al [37] also did similar work. Rosolia et al [38] designed a two-stage nonlinear nonconvex control approach, including an outer-loop nonlinear MPC and a model based on an inner linear feedback controller, for autonomous vehicles driving during highway cruise conditions.…”

Section: Autonomous Driving Studymentioning

confidence: 60%

Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections

Zhang

¹

,

Xie

²

,

Wang

³

et al. 2018

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With the coming of intelligent vehicles and vehicular communication, Intelligent Transportation Systems (ITS) of connected vehicles are emerging and now evolving to Cooperative-ITS (C-ITS), as service platforms for smart cities. Considering new service properties, the autonomous cooperation of such vehicles has exhibited novel QoS features that imply new requirements: guaranteeing the traffic efficiency of any emergent vehicle while trying to promote the throughput at an intersection. So, after analyzing the classic reservation-based cooperation mechanisms, new QoS-oriented cooperation methods and policies are studied in this work. Concretely, several models of related traffic objects we have proposed are firstly introduced briefly. Then, the scheduling policies of vehicles approaching an intersection have been presented, including three existing policies (FAFP-SV, FAFP-SQ, and HQEP-SV) and five new polices (FAFP-SQ-SV, FAFP-MQ, HWFP-SQ, HWFP-SQ-SV, HWFP-MQ). These policies combine two major factors: vehicular priority for scheduling and concurrency in traffics. The first one includes the vehicular arrival-time, priority mapped to QoS, and the weight of reserved vehicles on a lane etc. In addition, the second refers to schedule a platoon rather than single vehicle each time, or platoons on different lanes instead of one platoon on only one lane. All these policies have been implemented, and further, verified within the parameter-configurable traffic simulator QoS-CITS (v2.1) we designed and developed with C#. Abundant experiments have been conducted with configured typical traffic scenes, and experimental results show that HWFP-SQ-SV and HWFP-MQ can guarantee both the QoS of emergent vehicles and traffic throughput better than other six policies.

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“…Vehicle dynamics refer to vehicle motion in the lateral and longitudinal directions [8]. Lateral dynamics or transverse dynamics are controlled by the vehicle stability analysis, which is referred to the vehicle handling stability and side slip, which caused by the tire lateral force, yawing and roll motion [8,9].…”

Section: Vehicle Dynamics Modelmentioning

confidence: 99%

“…Vehicle dynamics refer to vehicle motion in the lateral and longitudinal directions [8]. Lateral dynamics or transverse dynamics are controlled by the vehicle stability analysis, which is referred to the vehicle handling stability and side slip, which caused by the tire lateral force, yawing and roll motion [8,9]. Longitudinal dynamics concern with the vehicle behavior while driving such as accelerating, braking and ride comfort such as wheels traction on different road surfaces under different conditions [8,9].…”

Section: Vehicle Dynamics Modelmentioning

confidence: 99%

“…Lateral dynamics or transverse dynamics are controlled by the vehicle stability analysis, which is referred to the vehicle handling stability and side slip, which caused by the tire lateral force, yawing and roll motion [8,9]. Longitudinal dynamics concern with the vehicle behavior while driving such as accelerating, braking and ride comfort such as wheels traction on different road surfaces under different conditions [8,9]. A simple nonlinear model to describe the dynamics of the vehicle using four states is shown below Heading angle of the car (0 when the car facing east, counterclockwise positive) is given by: 𝜃 = (𝑡𝑎𝑛(𝛿)/𝑐𝐿) * 𝑆 + (𝑆 * (𝑡𝑎𝑛(𝛿) 2 ) + 1)/𝑐𝐿) * 𝛿 (3) cL refers to car length.…”

Section: Vehicle Dynamics Modelmentioning

confidence: 99%

See 1 more Smart Citation

Untitled

2020

IJICSS

0

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Motion planning for autonomous vehicles in urban environments is an important and challenging task due to the vehicle dynamic constraints, involvement of stationary and moving obstacles, other vehicles and unpredictable pedestrian movements. The proposed algorithm will present a real time motion planning scheme of autonomous vehicles. Also, will allow autonomous vehicles to avoid collisions with multi stationary obstacles. A linear model predictive control (MPC) algorithm using Laguerre Functions is developed and used where the controller manipulated variables are the acceleration and the front wheels steering angle. Knowledge of the surrounding environment, the state of the autonomous vehicle and other constraints of the MPC are incorporated in the prediction model of the controller to predict safe trajectory for the vehicle and to avoid collision with other obstacles. The proposed algorithm will improve autonomous vehicles navigation behavior in unstructured environments.

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“…Because it considers the future shape of the path while computing the control signal during prediction horizon overall circumstances. Also, it takes into account the dynamic limitation of the vehicle while implementing the vehicle constraints [7,8]. There are several approaches to designing a Model Predictive Controller.…”

Section: Development Of Model Predictive Controlmentioning

confidence: 99%

Development a Real Time Stationary Obstacles Avoidance Scheme for Autonomous Vehicle Navigation System

Al-Salah¹,

Zein-Sabatto²

2020

IJICSS

0

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Motion planning for autonomous vehicles in urban environments is an important and challenging task due to the vehicle dynamic constraints, involvement of stationary and moving obstacles, other vehicles and unpredictable pedestrian movements. The proposed algorithm will present a real time motion planning scheme of autonomous vehicles. Also, will allow autonomous vehicles to avoid collisions with multi stationary obstacles. A linear model predictive control (MPC) algorithm using Laguerre Functions is developed and used where the controller manipulated variables are the acceleration and the front wheels steering angle. Knowledge of the surrounding environment, the state of the autonomous vehicle and other constraints of the MPC are incorporated in the prediction model of the controller to predict safe trajectory for the vehicle and to avoid collision with other obstacles. The proposed algorithm will improve autonomous vehicles navigation behavior in unstructured environments.

show abstract

Centralized model predictive control of autonomous driving vehicles with Lyapunov stability

Cited by 8 publications

References 13 publications

Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections

Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections

Untitled

Development a Real Time Stationary Obstacles Avoidance Scheme for Autonomous Vehicle Navigation System

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