A model predictive combined planning and control approach for guidance of automated vehicles

Götte, Christian; Keller, Martin; Hass, Carsten; Glander, Karl-Heinz; Seewald, Alois; Bertram, Torsten

doi:10.1109/icves.2015.7396896

Cited by 19 publications

(9 citation statements)

References 12 publications

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“…The length of the major and minor axis of the ellipse around ith parked car are r i a and r i b , respectively. The areas beyond sidewalls are also expressed as prohibited areas represented with a logarithmic function as explained in the next subsection (19).…”

Section: Input and Safety Constraintsmentioning

confidence: 99%

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Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving

Muraleedharan

Okuda

Suzuki

2022

IEEE Trans. Intell. Veh.

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Section: Input and Safety Constraintsmentioning

confidence: 99%

“…Combining these two into one problem formulation clearly has the benefit of a lower computational burden by removing redundancies in vehicle dynamics calculations. Such combined problems are often referred to as simultaneous motion planning and control (SMPLC) problem [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving

Muraleedharan

Okuda

Suzuki

2022

IEEE Trans. Intell. Veh.

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“…y e =v y + Vθ e (11) where θ e , the difference of the angle between the vehicle heading and the tangent of the driving lane, is also assumed to be small. The benefit to consider the error system form the center of the driving lane is that the obtained formula can be applied not only to the straight road but also to the curvy road by considering the curvature of the road.…”

Section: Input and Statementioning

confidence: 99%

“…Although the increase of the considered constraints for path planning may resolve such problem, this results in the redundant computation in the implementation of the path planning and motion control. In order to solve this problem, the idea of simultaneous path planning and motion control (SPPMC) is considered and attracting great attention [11]- [15]. In SPPMC, a unified optimization problem to find the optimal control input is addressed by considering not only the vehicle dynamics but also the goal of the task and various constraints such as the physical limitations, safety constraints and so on.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Realization of Decision, Planning and Control for Lane-Changing Behavior Using Nonlinear Model Predictive Control

Okuda

Sugie

Suzuki

et al. 2020

IEICE Trans. Inf. & Syst.

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Path planning and motion control are fundamental components to realize safe and reliable autonomous driving. The discrimination of the role of these two components, however, is somewhat obscure because of strong mathematical interaction between these two components. This often results in a redundant computation in the implementation. One of attracting idea to overcome this redundancy is a simultaneous path planning and motion control (SPPMC) based on a model predictive control framework. SPPMC finds the optimal control input considering not only the vehicle dynamics but also the various constraints which reflect the physical limitations, safety constraints and so on to achieve the goal of a given behavior. In driving in the real traffic environment, decision making has also strong interaction with planning and control. This is much more emphasized in the case that several tasks are switched in some context to realize higherlevel tasks. This paper presents a basic idea to integrate decision making, path planning and motion control which is able to be executed in realtime. In particular, lane-changing behavior together with the decision of its initiation is selected as the target task. The proposed idea is based on the nonlinear model predictive control and appropriate switching of the cost function and constraints in it. As the result, the decision of the initiation, planning, and control of the lane-changing behavior are achieved by solving a single optimization problem under several constraints such as safety. The validity of the proposed method is tested by using a vehicle simulator.

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“…In August 2013, Mercedes Benz used this approach during the Berta Benz drive, were a mostly autonomous vehicle drove the Bertha Benz Memorial Route [11]. Furthermore, there are model predictive procedures [12,13]. They integrate dynamic models into the planning to make them more accurate and to ensure the feasibility of the path.…”

Section: Related Workmentioning

confidence: 99%

Path Planning for Highly Automated Driving on Embedded GPUs

Fickenscher

Schmidt

Hannig

et al. 2018

JLPEA

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The sector of autonomous driving gains more and more importance for the car makers. A key enabler of such systems is the planning of the path the vehicle should take, but it can be very computationally burdensome finding a good one. Here, new architectures in ECU are required, such as GPU, because standard processors struggle to provide enough computing power. In this work, we present a novel parallelization of a path planning algorithm. We show how many paths can be reasonably planned under real-time requirements and how they can be rated. As an evaluation platform, an Nvidia Jetson board equipped with a Tegra K1 SoC was used, whose GPU is also employed in the zFAS ECU of the AUDI AG.

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A model predictive combined planning and control approach for guidance of automated vehicles

Cited by 19 publications

References 12 publications

Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving

Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving

Simultaneous Realization of Decision, Planning and Control for Lane-Changing Behavior Using Nonlinear Model Predictive Control

Path Planning for Highly Automated Driving on Embedded GPUs

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