Generic Approach to Optimized Placement of Smart Roadside Infrastructure Sensors Using 3D Digital Maps

Kloeker, Laurent; Quakernack, Julian; Lampe, Bastian; Eckstein, Lutz

doi:10.1109/itsc55140.2022.9921838

Cited by 7 publications

(4 citation statements)

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“…Several approaches consider the problem of monitoring a 3-D space, but with significant limitations affecting the applicability of these approaches to localizing small UAVs in large critical areas, such as those considered here. In [5], [11], [14], [27], [28], and [29], the candidate points for placement all lie on a 3-D surface; in [3], [4], [30], [31], [32], and [33], admissible sensor positions or points to be monitored (or both) belong to finite sets in the 3-D space. In particular, in most existing 3-D approaches, the RoI is discretized in cells.…”

Section: State Of the Artmentioning

confidence: 99%

Optimizing Fault-Tolerant Quality-Guaranteed Sensor Deployments for UAV Localization in Critical Areas via Computational Geometry

Esposito,

Mancini,

Tronci

2024

IEEE Trans. Syst. Man Cybern, Syst.

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The increasing spreading of small commercial unmanned aerial vehicles (UAVs, also known as drones) presents serious threats for critical areas, such as airports, power plants, and governmental and military facilities. In fact, such UAVs can easily disturb or jam radio communications, collide with other flying objects, perform espionage activity, and carry offensive payloads, e.g., weapons or explosives. A central problem when designing surveillance solutions for the localization of unauthorized UAVs in critical areas is to decide how many triangulating sensors to use, and where to deploy them to optimize both coverage and cost effectiveness. In this article, we compute deployments of triangulating sensors for UAV localization, optimizing a given blend of metrics, namely: coverage under multiple sensing quality levels, cost-effectiveness, and fault-tolerance. We focus on large, complex three-dimensional (3-D) regions, which exhibit obstacles (e.g., buildings), varying terrain elevation, different coverage priorities, and constraints on possible sensors placement. Our novel approach relies on computational geometry and statistical model checking and enables the effective use of offthe-shelf AI-based black-box optimizers. Moreover, our method allows us to compute a closed-form, analytical representation of the region uncovered by a sensor deployment, which provides the means for rigorous, formal certification of the quality of the latter. We show the practical feasibility of our approach by computing optimal sensor deployments for UAV localization in two large, complex 3-D critical regions, the Rome Leonardo Da Vinci International Airport (FCO) and the Vienna International Center (VIC), using NOMAD as our state-of-the-art underlying optimization engine. Results show that we can compute optimal sensor deployments within a few hours on a standard workstation and within minutes on a small parallel infrastructure.

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Section: State Of the Artmentioning

confidence: 99%

Optimizing Fault-Tolerant Quality-Guaranteed Sensor Deployments for UAV Localization in Critical Areas via Computational Geometry

Esposito,

Mancini,

Tronci

2024

IEEE Trans. Syst. Man Cybern, Syst.

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“…Infrastructural LiDAR data can further be extended with point clouds recorded by vehicles. There are several papers for this extended task [11,41,42], which work with simulated data or the DAIR-V2X dataset [10]. Further works in this direction have been published, but since this deviates from the task of this work, we will leave it at this point with this selection.…”

Section: D Object Detection With Infrastructural Lidar Sensorsmentioning

confidence: 99%

“…Using the ego-vehicle-based data for training would be a big domain shift to this setup. Although there are public datasets with infrastructural sensors [7][8][9][10], these are rather limited in availability, size and quality [11,12]. Furthermore, due to the domain shift, it is not possible to use these for training and direct inference.…”

Section: Introductionmentioning

confidence: 99%

Exploring 3D Object Detection for Autonomous Factory Driving: Advanced Research on Handling Limited Annotations with Ground Truth Sampling Augmentation

Reuse,

Amende,

Simon

et al. 2024

The 2nd AAAI Workshop on Artificial Intelligence With Biased or Scarce Data (AIBSD)

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“…A. Qu et al [20] proposed implementing the environment in a simulation and placing sensors, designating candidate locations, and optimizing them using the point cloud projected on the road surface while excluding buildings, sidewalks, etc. L. Kloeker et al [21] proposed optimizing lidar placement in infrastructure by utilizing a 3D digital map. They divided the road into triangles based on the OpenDRIVE [22] format map and optimized lidar placement by considering the number of points projected onto these triangles.…”

Section: Introductionmentioning

confidence: 99%

Placement Method of Multiple Lidars for Roadside Infrastructure in Urban Environments

Kim,

Jo,

Yun

et al. 2023

Sensors

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Sensors on autonomous vehicles have inherent physical constraints. To address these limitations, several studies have been conducted to enhance sensing capabilities by establishing wireless communication between infrastructure and autonomous vehicles. Various sensors are strategically positioned within the road infrastructure, providing essential sensory data to these vehicles. The primary challenge lies in sensor placement, as it necessitates identifying optimal locations that minimize blind spots while maximizing the sensor’s coverage area. Therefore, to solve this problem, a method for positioning multiple sensor systems in road infrastructure is proposed. By introducing a voxel grid, the problem is formulated as an optimization challenge, and a genetic algorithm is employed to find a solution. Experimental findings using lidar sensors are presented to demonstrate the efficacy of this proposed approach.

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Generic Approach to Optimized Placement of Smart Roadside Infrastructure Sensors Using 3D Digital Maps

Cited by 7 publications

References 0 publications

Optimizing Fault-Tolerant Quality-Guaranteed Sensor Deployments for UAV Localization in Critical Areas via Computational Geometry

Optimizing Fault-Tolerant Quality-Guaranteed Sensor Deployments for UAV Localization in Critical Areas via Computational Geometry

Exploring 3D Object Detection for Autonomous Factory Driving: Advanced Research on Handling Limited Annotations with Ground Truth Sampling Augmentation

Placement Method of Multiple Lidars for Roadside Infrastructure in Urban Environments

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