Speed Planning and Control of Low-Speed Autonomous Vehicles at Intersections

2019 ◽  
Vol 2019 (0) ◽  
pp. 2A1-E01
Author(s):  
Hidetoshi TAKEMURA ◽  
Pongsathorn RAKSINCHAROENSAK
Keyword(s):  
Autonomous Vehicles ◽  
Low Speed ◽  
Planning And Control ◽  
And Control

scholarly journals Path and Control Planning for Autonomous Vehicles in Restricted Space and Low Speed

2020 ◽  
Author(s):  
Maksym Diachuk ◽  
Said Easa ◽  
Joel Bannis
Keyword(s):  
Autonomous Vehicles ◽  
Optimal Solution ◽  
Control Laws ◽  
Low Speed ◽  
Output State ◽  
Original Algorithm ◽  
Nonlinear Motion ◽  
Space Of Constant Curvature ◽  
Kinematic Models ◽  
And Control

The paper presents models of path and control planning for parking, docking, and movement of autonomous vehicles at low speeds considering space constraints. Given the low speed of motion, and in order to test and approve the proposed algorithms, vehicle kinematic models are used. Recent works on the development of parking algorithms for autonomous vehicles are reviewed. Bicycle kinematic models for vehicle motion are considered for three basic types of vehicles: passenger car, long wheelbase truck, and articulated vehicles with and without steered semitrailer axes. Mathematical descriptions of systems of differential equations in matrix form and expressions for determining the linearization elements of nonlinear motion equations that increase the speed of finding the optimal solution are presented. Options are proposed for describing the interaction of vehicle overall dimensions with the space boundaries, within which a maneuver should be performed. An original algorithm that considers numerous constraints is developed for determining vehicle permissible positions within the closed boundaries of the parking area, which are directly used in the iterative process of searching for the optimal plan solution using nonlinear model predictive control (NMPC). The process of using NMPC to find the best trajectories and control laws while moving in a semi-limited space of constant curvature (turnabouts, roundabouts) are described. Simulation tests were used to validate the proposed models for both constrained and unconstrained conditions and the output (state-space) and control parameters' dependencies are shown. The proposed models represent an initial effort to model the movement of autonomous vehicles for parking and has the potential for other highway applications.

scholarly journals Path and Control Planning for Autonomous Vehicles in Restricted Space and Low Speed

2020 ◽  
Vol 5 (5) ◽  
pp. 42 ◽  
Author(s):  
Maksym Diachuk ◽  
Said M. Easa ◽  
Joel Bannis
Keyword(s):  
Autonomous Vehicles ◽  
Optimal Solution ◽  
Control Laws ◽  
Low Speed ◽  
Output State ◽  
Original Algorithm ◽  
Nonlinear Motion ◽  
Space Of Constant Curvature ◽  
Kinematic Models ◽  
And Control

This paper presents models of path and control planning for the parking, docking, and movement of autonomous vehicles at low speeds, considering space constraints. Given the low speed of motion, and in order to test and approve the proposed algorithms, vehicle kinematic models are used. Recent works on the development of parking algorithms for autonomous vehicles are reviewed. Bicycle kinematic models for vehicle motion are considered for three basic types of vehicles: passenger car, long wheelbase truck, and articulated vehicles with and without steered semitrailer axes. Mathematical descriptions of systems of differential equations in matrix form and expressions for determining the linearization elements of nonlinear motion equations that increase the speed of finding the optimal solution are presented. Options are proposed for describing the interaction of vehicle overall dimensions with the space boundaries, within which a maneuver should be performed. An original algorithm that considers numerous constraints is developed for determining vehicle permissible positions within the closed boundaries of the parking area, which are directly used in the iterative process of searching for the optimal plan solution using nonlinear model predictive control (NMPC). The process of using NMPC to find the best trajectories and control laws while moving in a semi-limited space of constant curvature (turnabouts, roundabouts) are described. Simulation tests were used to validate the proposed models for both constrained and unconstrained conditions and the output (state-space) and control parameters’ dependencies are shown. The proposed models represent an initial effort to model the movement of autonomous vehicles for parking and have the potential for other highway applications.

scholarly journals Autonomous Driving—A Crash Explained in Detail

2019 ◽  
Vol 9 (23) ◽  
pp. 5126 ◽  
Author(s):  
Betz ◽  
Heilmeier ◽  
Wischnewski ◽  
Stahl ◽  
Lienkamp
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Software Test ◽  
Planning And Control ◽  
Modular Software ◽  
Fatal Crash ◽  
Detailed Explanation ◽  
A Minor ◽  
And Control

Since 2017, a research team from the Technical University of Munich has developed a software stack for autonomous driving. The software was used to participate in the Roborace Season Alpha Championship. The championship aims to achieve autonomous race cars competing with different software stacks against each other. In May 2019, during a software test in Modena, Italy, the greatest danger in autonomous driving became reality: A minor change in environmental influences led an extensively tested software to crash into a barrier at speed. Crashes with autonomous vehicles have happened before but a detailed explanation of why software failed and what part of the software was not working correctly is missing in research articles. In this paper we present a general method that can be used to display an autonomous vehicle disengagement to explain in detail what happened. This method is then used to display and explain the crash from Modena. Firstly a brief introduction into the modular software stack that was used in the Modena event, consisting of three individual parts—perception, planning, and control—is given. Furthermore, the circumstancescausing the crash are elaborated in detail. By presented and explaining in detail which softwarepart failed and contributed to the crash we can discuss further software improvements. As a result, we present necessary functions that need to be integrated in an autonomous driving software stack to prevent such a vehicle behavior causing a fatal crash. In addition we suggest an enhancement of the current disengagement reports for autonomous driving regarding a detailed explanation of the software part that was causing the disengagement. In the outlook of this paper we present two additional software functions for assessing the tire and control performance of the vehicle to enhance the autonomous.

System and Experiments of Model-Driven Motion Planning and Control for Autonomous Vehicles

2021 ◽  
pp. 1-14
Author(s):  
Shaobing Xu ◽  
Robert Zidek ◽  
Zhong Cao ◽  
Pingping Lu ◽  
Xinpeng Wang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Autonomous Vehicles ◽  
Model Driven ◽  
Planning And Control ◽  
And Control

scholarly journals A dϕ-Strategy: Facilitating Dual-Formation Control of a Virtually Connected Team

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Bibhya Sharma ◽  
Jito Vanualailai ◽  
Avinesh Prasad
Keyword(s):  
Autonomous Vehicles ◽  
Formation Control ◽  
Field Method ◽  
Mobile Manipulators ◽  
End Effector ◽  
Planning And Control ◽  
Potential Field Method ◽  
Control Scheme ◽  
And Control ◽  
First Time

This paper describes the design of new centralized acceleration-based controllers for the multitask problem of motion planning and control of a coordinated lead-carrier team fixed in a dual-formation within an obstacle-ridden environment. A dϕ-strategy, where d and ϕ are Euclidean measures with respect to the lead robot, is developed to ensure virtual connectivity of the carrier robots to the lead robot. This connectivity, built into the system itself, inherently ensures globally rigid formation between each lead-carrier pair of the team. Moreover, a combination of target configuration, dϕ-strategy, orientation consensus, and avoidance of end-effector of robots results in a second, locally rigid formation (not infinitesimally rigid). Therefore, for the first time, a dual-formation control problem of a lead-carrier team of mobile manipulators is considered. This and other kinodynamic constraints have been treated simultaneously via the overarching Lyapunov-based control scheme, essentially a potential field method favored in the field of robotics. The formulation of this new scheme, demonstrated effectively via computer simulations, is timely, given that the current proposed engineering solutions, allowing autonomous vehicles on public roads, include the development of special lanes imbued with special sensors and wireless technologies.

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