Barnes Hospital Bulletin

https://digitalcommons.wustl.edu/bjc_barnes_bulletin/1206/thumbnail.jp

iValet – A smart parking reservation system

In the current time with the continuously increasing population in the world generally and in big cities like Dubai particularly, many problems arise related to traffic and transportation. Due to covid-19 virus spread pandemic, WHO is submitting different regulations and recommendation for the need of social distancing along with avoiding any activities that might increase the possibility of the virus spread and minimize the number of infected people to save more lives. The need for decreasing the usage of the public transportation in Dubai have created parking issues in the city, streets, and buildings. One of the preferred solutions by people to overcome the long searching time for a parking lot and to reduce the fuel usage is to use the valet parking service in different city buildings, but many of them now are scared to allow strangers to use their car in the valet service. In this regard, the idea has emerged of having a smart parking reservation system that is connected with the RTA and the traffic police. The system consists of a hardware part implemented in the parking lot, and a software part where the user can search for a vacant parking spot and reserve it with in-advance payment using his/her smart phone

LineMarkNet: Line Landmark Detection for Valet Parking

We aim for accurate and efficient line landmark detection for valet parking, which is a long-standing yet unsolved problem in autonomous driving. To this end, we present a deep line landmark detection system where we carefully design the modules to be lightweight. Specifically, we first empirically design four general line landmarks including three physical lines and one novel mental line. The four line landmarks are effective for valet parking. We then develop a deep network (LineMarkNet) to detect line landmarks from surround-view cameras where we, via the pre-calibrated homography, fuse context from four separate cameras into the unified bird-eye-view (BEV) space, specifically we fuse the surroundview features and BEV features, then employ the multi-task decoder to detect multiple line landmarks where we apply the center-based strategy for object detection task, and design our graph transformer to enhance the vision transformer with hierarchical level graph reasoning for semantic segmentation task. At last, we further parameterize the detected line landmarks (e.g., intercept-slope form) whereby a novel filtering backend incorporates temporal and multi-view consistency to achieve smooth and stable detection. Moreover, we annotate a large-scale dataset to validate our method. Experimental results show that our framework achieves the enhanced performance compared with several line detection methods and validate the multi-task network's efficiency about the real-time line landmark detection on the Qualcomm 820A platform while meantime keeps superior accuracy, with our deep line landmark detection system.Comment: 29 pages, 12 figure

Model Predictive Control System Design of a passenger car for Valet Parking Scenario

A recent expansion of passenger cars’ automated functions has led to increasingly challenging design problems for the engineers. Among this the development of Automated Valet Parking is the latest addition. The system represents the next evolution of automated system giving the vehicle greater autonomy: the efforts of most automotive OEMs go towards achieving market deployment of such automated function. To this end the focus of each OEM is on taking part to this competitive endeavor and succeed by developing a proprietary solution with the support of hardware and software suppliers. Within this framework the present work aims at developing an effective control strategy for the considered scenarios. In order to reach this goal a Model Predictive Control approach is employed taking advantage of previous works within the automotive OEM in the automated driving field. The control algorithm is developed in a Simulink® simulation according to the requirements of the application and tested; results show the control strategy successfully drives the vehicle on the predefined path

Integer Programming Model for Automated Valet Parking

Kuna parkimine on autode hulga suurenemisega ja linnastumise tihenemisega üha keerulisem probleem, muutub selle kõrgtehnoloogiline lahendamine\n\rotstarbekaks. Üks pakutud lahendus on automaatparkla, kus autodega ei sõideta\n\roma parkimiskohta, vaid autod toimetatakse parkimiskohta ja tagasi\n\rspetsiaalsete robotite poolt. Selline kõrgtehnoloogiline lahendus annab meile\n\rpalju erinevaid optimiseerimisülesandeid ja võimalusi, millest ühte\n\rkonkreetset käsitleme käesolevas töös kasutades täisarvulise planeerimise\n\rmeetodeid, mida saab lahendada juba eksisteerivate analüütiliste lahendajatega.\n\rKäesolevas töös käsitletakse ühte kindlat võimalikku automaatparkla\n\rimplementatsiooni ja tuletatakse täisarvulise planeerimise mudel selle\n\rlahendamiseks. Varasemad teoreetilised tulemused on näidanud, et isegi\n\rlihtsustatud variandid sellest probleemist saavad olla APX-keerukusega. Kasutades Gurobi lahendajat leiti optimaalne lahendus näidisjuhtude jaoks. Mudelit võrreldi teise täisarvulise planeerimise mudeliga algoritmide ja teooria teadusgrupist, mis andis kindlust ja võrdlusmaterjali mudeli toimimiseks.As parking becomes a more and more complex problem with the number of cars and\n\rcity density, more complex solutions can be used to rectify it. One of possible\n\rsolutions for parking is automated valet parking, where cars are not driven to\n\rparking place by humans but are carried by specially designed robots. Such\n\rsolution presents us many possible optimization problems, one of which is\n\raddressed in this work using Integer Programming models, that can by solved\n\rusing off-the-shelf solvers. We look at a specific possible implementation of\n\rautomated valet parking and succesfully design an Integer Programming model to\n\rsolve it. Existing theoretical results have shown that even simplified cases of\n\rthe problem can be APX-hard. Using Gurobi,\n\roptimal solution was found for sample cases. The model is compared to another\n\rinteger programming model from Algorithms and Theory research group, which\n\rprovides comparison and verification for the model performance

Towards Autonomy: Cost-effective Scheduling for Long-range Autonomous Valet Parking (LAVP)

Continuous and effective developments in Autonomous Vehicles (AVs) are happening on daily basis. Industries nowadays, are interested in introducing less costly and highly controllable AVs to public. Current so-called AVP solutions are still limited to a very short range (e.g., even only work at the entrance of car parks). This paper proposes a parking scheduling scheme for long-range AVP (LAVP) case, by considering mobility of Autonomous Vehicles (AVs), fuel consumption and journey time. In LAVP, Car Parks (CPs) are used to accommodate increasing numbers of AVs, and placed outside city center, in order to avoid traffic congestions and ensure road safety in public places. Furthermore, with positioning of reference points to guide user-centric long-term driving and drop-off/pick-up passengers, simulation results under the Helsinki city scenario shows the benefits of LAVP. The advantage of LAVP system is also reflected through both analysis and simulation

Surround-view Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-insensitive Multi-task Framework

Surround-view fisheye perception under valet parking scenes is fundamental and crucial in autonomous driving. Environmental conditions in parking lots perform differently from the common public datasets, such as imperfect light and opacity, which substantially impacts on perception performance. Most existing networks based on public datasets may generalize suboptimal results on these valet parking scenes, also affected by the fisheye distortion. In this article, we introduce a new large-scale fisheye dataset called Fisheye Parking Dataset(FPD) to promote the research in dealing with diverse real-world surround-view parking cases. Notably, our compiled FPD exhibits excellent characteristics for different surround-view perception tasks. In addition, we also propose our real-time distortion-insensitive multi-task framework Fisheye Perception Network (FPNet), which improves the surround-view fisheye BEV perception by enhancing the fisheye distortion operation and multi-task lightweight designs. Extensive experiments validate the effectiveness of our approach and the dataset's exceptional generalizability.Comment: 12 pages, 11 figure