Impact of out-of-sequence measurements on the joint integrated probabilistic data association filter for vehicle safety systems

Westenberger, Antje; Duraisamy, Bharanidhar; Münz, Michael; Muntzinger, Marc; Fritzsche, Martin; Dietmayer, Klaus

doi:10.1109/ivs.2012.6232180

Cited by 10 publications

(4 citation statements)

References 16 publications

(21 reference statements)

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“…Future works will need a principled object-level fusion of vision and active sensors for vehicle detection [177]. Such an information fusion could reduce estimation covariance and enhance robustness, although the asynchronous nature of the multiple modalities will need to be handled [190].…”

Section: A Vehicle Detectionmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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Abstract-This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

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Section: A Vehicle Detectionmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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“…Future works will need a principled, object-level fusion of vision and radar/lidar for vehicle detection [81]. Such an information fusion could reduce estimation covariance and enhance robustness, although the asynchronous nature of the multiple modalities will need to be handled [82].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

A review of recent developments in vision-based vehicle detection

Sivaraman¹,

Trivedi²

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-This document provides a review of the past decade's literature in on-road vision-based vehicle detection. Over the past decade, vision-based surround perception has matured significantly from its infancy. We detail advances in vehicle detection, discussing representative works from the monocular and stereo-vision domains. We provide discussion on the state-of-the-art, and provide perspective on future research directions in the field.

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“…The out-of-sequence measurement (OOSM) problem is one of the challenges in large-scale distributed sensor networks [ 6 ]. The AMI architecture contains thousands of electrical measuring devices.…”

Section: Introductionmentioning

confidence: 99%

“…In the applications, Besada-Portas studied the out-of-sequence problem in mobile robot localization [ 26 ]. Klaus [ 6 ] applied OOSM for multi-sensor fusion in vehicles and proposed joint integrated probabilistic data association (JIPDA) fusion. Schueller [ 27 ] provided a temporal solution to calibrate the data sequence in an advanced driver assistance system.…”

Section: Introductionmentioning

confidence: 99%

Data-Aware Retrodiction for Asynchronous Harmonic Measurement in a Cyber-Physical Energy System

Liu

Wang

Liu

et al. 2016

Sensors

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Cyber-physical energy systems provide a networked solution for safety, reliability and efficiency problems in smart grids. On the demand side, the secure and trustworthy energy supply requires real-time supervising and online power quality assessing. Harmonics measurement is necessary in power quality evaluation. However, under the large-scale distributed metering architecture, harmonic measurement faces the out-of-sequence measurement (OOSM) problem, which is the result of latencies in sensing or the communication process and brings deviations in data fusion. This paper depicts a distributed measurement network for large-scale asynchronous harmonic analysis and exploits a nonlinear autoregressive model with exogenous inputs (NARX) network to reorder the out-of-sequence measuring data. The NARX network gets the characteristics of the electrical harmonics from practical data rather than the kinematic equations. Thus, the data-aware network approximates the behavior of the practical electrical parameter with real-time data and improves the retrodiction accuracy. Theoretical analysis demonstrates that the data-aware method maintains a reasonable consumption of computing resources. Experiments on a practical testbed of a cyber-physical system are implemented, and harmonic measurement and analysis accuracy are adopted to evaluate the measuring mechanism under a distributed metering network. Results demonstrate an improvement of the harmonics analysis precision and validate the asynchronous measuring method in cyber-physical energy systems.

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Impact of out-of-sequence measurements on the joint integrated probabilistic data association filter for vehicle safety systems

Cited by 10 publications

References 16 publications

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

A review of recent developments in vision-based vehicle detection

Data-Aware Retrodiction for Asynchronous Harmonic Measurement in a Cyber-Physical Energy System

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