Distributed Multi-Target Tracking for Autonomous Vehicle Fleets

Shorinwa, Ola; Yu, Javier; Halsted, Trevor; Koufos, Alex; Schwager, Mac

doi:10.1109/icra40945.2020.9197241

Cited by 19 publications

(12 citation statements)

References 24 publications

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“…Experimental Design. In order to evaluate our method in consideration of road topology, enriched traffic environment, vehicle dynamics and AI-based sensor perception models from raw data to object tracking, we demonstrate the BiFNoE and the noise estimation service in the open-source simulator CARLA [31] with 50 CAVs and 50 non-CAVs, which is similar to the scenario studied in [13]. All CAVs locally process the video in 10 fps and share the sensor data as object lists with the edge server and the neighbor CAVs simultaneously.…”

Section: Carla-based Evaluationmentioning

confidence: 99%

Edge-Aided Sensor Data Sharing in Vehicular Communication Networks

Song¹,

Hegde²,

Senel³

et al. 2022

Preprint

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Sensor data sharing in vehicular networks can significantly improve the range and accuracy of environmental perception for connected automated vehicles. Different concepts and schemes for dissemination and fusion of sensor data have been developed. It is common to these schemes that measurement errors of the sensors impair the perception quality and can result in road traffic accidents. Specifically, when the measurement error from the sensors -also referred as measurement noiseis unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors. In this paper, we consider sensor data sharing and fusion in a vehicular network with both, vehicle-to-infrastructure and vehicle-to-vehicle communication. We propose a method, named Bidirectional Feedback Noise Estimation (BiFNoE), in which an edge server collects and caches sensor measurement data from vehicles. The edge estimates the noise and the targets alternately in double dynamic sliding time windows and enhances the distributed cooperative environment sensing at each vehicle with low communication costs. We evaluate the proposed algorithm and data dissemination strategy in an application scenario by simulation and show that the perception accuracy is on average improved by around 80 % with only 12 kbps uplink and 28 kbps downlink bandwidth.

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Section: Carla-based Evaluationmentioning

confidence: 99%

Edge-Aided Sensor Data Sharing in Vehicular Communication Networks

Song¹,

Hegde²,

Senel³

et al. 2022

Preprint

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“…However, these works require a central node for the dual variable updates. Other works employ the consensus ADMM variant without a central node [111] with other notable applications in target tracking [3], signal estimation [16], task assignment [112], motion planning [5], online learning [113], and parameter estimation in global navigation satellite systems [114]. Further applications of C-ADMM arise in trajectory tracking problems involving teams of robots using non-linear model predictive control [115] and in cooperative localization [116].…”

Section: Applications Of C-admmmentioning

confidence: 99%

“…Methods for performing the estimate in real-time through filtering and smoothing steps have been well studied, both in the centralized and distributed case [122]. An extended version of this multi-robot tracking problem is solved with distributed optimization in [3]. A rendering of a representative instance of this multi-robot tracking problem is shown in Figure 2.…”

Section: A Distributed Multi-drone Vehicle Trackingmentioning

confidence: 99%

“…While distributed optimization has been a longstanding topic of research in the optimization community (e.g., [1], [2]), its usage in robotics is limited to only a handful of examples. However, distributed optimization techniques have important implications for multi-robot systems, as we can cast many of the key tasks in this area, including cooperative estimation [3], multiagent learning [4], and collaborative motion planning [5], as distributed optimization problems. The distributed optimization formulation offers a flexible and powerful paradigm for deriving efficient and distributed algorithms for many multirobot problems.…”

Section: Introductionmentioning

confidence: 99%

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A Survey of Distributed Optimization Methods for Multi-Robot Systems

Halsted¹,

Shorinwa²,

Yu³

et al. 2021

Preprint

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Distributed optimization consists of multiple computation nodes working together to minimize a common objective function through local computation iterations and networkconstrained communication steps. In the context of robotics, distributed optimization algorithms can enable multi-robot systems to accomplish tasks in the absence of centralized coordination. We present a general framework for applying distributed optimization as a module in a robotics pipeline. We survey several classes of distributed optimization algorithms and assess their practical suitability for multi-robot applications. We further compare the performance of different classes of algorithms in simulations for three prototypical multi-robot problem scenarios. The Consensus Alternating Direction Method of Multipliers (C-ADMM) emerges as a particularly attractive and versatile distributed optimization method for multi-robot systems.

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“…In order to integrate driverless cars in urban traffics, their safe operation must be ensured from the presence of potential hazards such as human operated vehicles and pedestri-ans [2]. Besides having good perception systems, they must also have the ability to monitor the kinematic behaviour of the multiple obstacles.…”

Section: Introductionmentioning

confidence: 99%

Aggregated Euclidean Distances for a Fast and Robust Real-Time 3D-MOT

Sadli

Afkir²,

Hadid

et al. 2021

IEEE Sensors J.

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Autonomous driving systems must have the ability to monitor the kinematic behaviour of multiple obsta-cles. Therefore, 3D multi-object tracking (3D-MOT) is one of the crucial modules in autonomous driving to detect the presence of potential hazard movements such as human operated vehicles and pedestrians. In this work, we present a novel online 3D multi-tracking system that uses the Aggre-gated Euclidean Distances (AED) in data association module instead of using Intersection over Union (IoU) as a new metric. AED is used in order to obtain the relationship between predicted tracks and current object detections. There are several benefits from using AED in data association module. Firstly, it can reduce the system's complexity so that the execution time can be significantly reduced (as calculating Euclidean distances is much faster than obtaining 3D-IoU). Secondly, AED can provide distance measurement even when there is no overlaps between the predicted tracks and the current detections, while 3D-IoU produces zeros for non-overlapping cases. To demonstrate the validity of our proposed method, we performed extensive experiments on KITTI multi-tracking benchmark and nuScenes validation datasets. The experimental results are compared against the open-sourced state of the art 3D MOTs such as AB3DMOT, FANTrack, and mmMOT. Our method clearly outperforms the AB3DMOT baseline method and other methods in terms of accuracy and/or processing speed.

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Distributed Multi-Target Tracking for Autonomous Vehicle Fleets

Cited by 19 publications

References 24 publications

Edge-Aided Sensor Data Sharing in Vehicular Communication Networks

Edge-Aided Sensor Data Sharing in Vehicular Communication Networks

A Survey of Distributed Optimization Methods for Multi-Robot Systems

Aggregated Euclidean Distances for a Fast and Robust Real-Time 3D-MOT

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