k-Stacks: High-density valet parking for automated vehicles

Timpner, Julian; Friedrichs, Stephan; Balen, Johannes van; Wolf, Lars

doi:10.1109/ivs.2015.7225798

Cited by 22 publications

(13 citation statements)

References 12 publications

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“…Related works on AVP have covered the field of parking space optimization based on k-stacks [3], [4], and the security of user information [5]. Another important problem is the localization and mapping task in GPS-denied parking environments [6], and the motion planning problem.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Vehicle Control Framework With Application to Automated Valet Parking

Kneissl

Madhusudhanan

Molin

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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We introduce a distributed control method for coordinating multiple vehicles in the framework of an automated valet parking (AVP) system. The control functionality is distributed between an infrastructure server, called parking area management (PAM) system, and local autonomous vehicle control units. Via a vehicle-to-infrastructure (V2I) communication interface, model predictive control (MPC) decisions of the vehicles are shared with the coordination unit in the PAM. This unit in turn computes a coupling feedback which is shared with the vehicles. The control system is integrated in an automated test-system to cope with the high test requirements and short development cycles of highly automated systems. Evaluations conducted with the test-system show the functionality of the proposed distributed control method for multi-vehicle coordination. Results indicate safe coordination, and an efficiency increase compared to an uncoordinated method in an AVP simulation environment.

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Section: Introductionmentioning

confidence: 99%

“…coordination on predefined routes. This article focuses on the coordination phase after a target parking slot [3], [4], and a respective path [7] have been computed. The coordination task is decomposed into a local vehicle control and a global coordination task.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Vehicle Control Framework With Application to Automated Valet Parking

Kneissl

Madhusudhanan

Molin

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Three prior studies have studied aspects of the spatial efficiency of AV parking (Ferreira et al [42], Timpner et al [43], and Estepa et al [44]). Ferreira et al investigated "stacked" storage of AVs, in which a parked vehicle blocked in by other vehicles would be accessed by the blocking vehicles shunting themselves (without human input).…”

Section: Introductionmentioning

confidence: 99%

Capacity Impacts and Optimal Geometry of Automated Cars’ Surface Parking Facilities

Kong

Vine

Liu

2018

Journal of Advanced Transportation

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The impact of Automated Vehicles (AVs) on urban geography has been widely speculated, though there is little quantitative evidence in the literature to establish the magnitude of such effects. To quantify the impact of the greater precision of automated driving on the spatial efficiency of off-street parking facilities, we develop a mixed integer nonlinear model (solved via a branch-and-cut approach) and present comparisons against industry-standard requirements for human-driving operation. We demonstrate that gains on the order of 40-50% in spatial efficiency (parking spaces per unit area) are in principle achievable while ensuring that each parked vehicle is independently accessible. We further show that the large majority of these efficiency gains can be obtained under current automotive engineering practice in which only the front two wheels pivot. There is a need for standardized methods that take the parking supply of a city as an input and calculate both the aggregate (citywide) efficiency impacts of automated driving and the spatial distribution of the effects. This study is intended as an initial step towards this objective.

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“…Therefore, a lot of park assistant systems has been studied in industry and academia. A variety of sensors and infrastructure of the parking lot have been used to develop park assistant system (1)(2)(3)(4)(5) . Basically, passive safety system which detect obstacles while parking is the most commercialized parking assist system which is commercialized almost thirty years ago (6,7) .…”

Section: Introductionmentioning

confidence: 99%

Sampling Based Vehicle Motion Planning for Autonomous Valet Parking with Moving Obstacles

Jeong

Kim

Rim

et al. 2018

IJAE

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This paper describes a motion planning algorithm for unstructured dynamic environments with motion prediction for moving obstacles. The proposed algorithm is composed of the four steps: 1) target motion prediction; 2) drivable area decision 3) local path planning and 4) vehicle control. The target motion prediction is crucial parts for realizing autonomous valet parking system because many vehicles which search available parking lot exist simultaneously. To predict future motion of target, the intention of the target should be inferred first. Interacting multiple model (IMM) filter using two models has been used to infer the intention of the target. Based on the inferred intention, most appropriate model's results are used as a predicted trajectory of the target vehicle. After that, the drivable area is decided to avoid collision with static obstacles and moving targets using potential filed approach to assessment the risk. In this stage, pre-defined parking lot map which contains boundary of the parking lots and waypoints is used to define initial guess of the drivable area. Inside the drivable area, rapidly-exploring random tree (RRT) generate the desired local path while guaranteeing the real-time performance in dynamic environments. Finally, path tracking controller and speed controller calculate desired steering wheel and longitudinal acceleration input. The proposed motion planning algorithm is validated via MATLAB based computer simulation. Simulation results demonstrate the ability of the proposed motion planning algorithm for unconstructed dynamic environments to plan collision-free path which is appropriate in parking lot situations.

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k-Stacks: High-density valet parking for automated vehicles

Cited by 22 publications

References 12 publications

A Multi-Vehicle Control Framework With Application to Automated Valet Parking

A Multi-Vehicle Control Framework With Application to Automated Valet Parking

Capacity Impacts and Optimal Geometry of Automated Cars’ Surface Parking Facilities

Sampling Based Vehicle Motion Planning for Autonomous Valet Parking with Moving Obstacles

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