The Correlation between Vehicle Vertical Dynamics and Deep Learning-Based Visual Target State Estimation: A Sensitivity Study

Weber, Yannik; Kanarachos, Stratis

doi:10.3390/s19224870

Cited by 7 publications

(4 citation statements)

References 67 publications

(72 reference statements)

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“…By utilizing semantic class data from the profound layer and segregation data from the shallow layer, Ma prepared a straight relationship channel on a bunch of fixed convolution layers, and the coarse-to-fine technique is utilized to anticipate the area of the objective [ 8 ]. By extending the correlation filter of spatial regularization, Weber and Kanarachos et al put forward DeepSRDCF tracker, and furthermore explored the impact of convolutional highlights on target tracking [ 9 ].…”

Section: Target Tracking Methods Based On Deep Learningmentioning

confidence: 99%

Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence

Wang

2022

Computational Intelligence and Neuroscience

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Target tracking is an important subject in computer vision technology, which has developed rapidly in recent ten years, and its application have become wider and wider. In this process, it has transferred from a simple experimental tracking environment to a complex real scene where more challenges need to be solved. The rapid development of deep learning has promoted the research progress of digital vision. Target tracking technology is an important foundation of digital vision research, which makes it develop from academia to industry. In this paper, a method of target tracking using MDNet is introduced. Starting with the attention mechanism, two attention mechanisms are added to extract and integrate the better features. Case partitioning is used to reduce the investment of tracking module and minimize the network size during tracking, and its result can be prevented from getting worse. Finally, the experiment is analyzed in detail.

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Section: Target Tracking Methods Based On Deep Learningmentioning

confidence: 99%

Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence

Wang

2022

Computational Intelligence and Neuroscience

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“…Weber and Kanarachos [24] investigate the influence of the vertical dynamics of the ego-vehicle on the object detection performance. The authors analyze the influence of vertical dynamics induced by road bumps on object detection and object tracking.…”

Section: Performance Influencing Meta-informationmentioning

confidence: 99%

Identification and Explanation of Challenging Conditions for Camera-Based Object Detection of Automated Vehicles

Ponn

Kröger

Diermeyer

2020

Sensors

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For a safe market launch of automated vehicles, the risks of the overall system as well as the sub-components must be efficiently identified and evaluated. This also includes camera-based object detection using artificial intelligence algorithms. It is trivial and explainable that due to the principle of the camera, performance depends highly on the environmental conditions and can be poor, for example in heavy fog. However, there are other factors influencing the performance of camera-based object detection, which will be comprehensively investigated for the first time in this paper. Furthermore, a precise modeling of the detection performance and the explanation of individual detection results is not possible due to the artificial intelligence based algorithms used. Therefore, a modeling approach based on the investigated influence factors is proposed and the newly developed SHapley Additive exPlanations (SHAP) approach is adopted to analyze and explain the detection performance of different object detection algorithms. The results show that many influence factors such as the relative rotation of an object towards the camera or the position of an object on the image have basically the same influence on the detection performance regardless of the detection algorithm used. In particular, the revealed weaknesses of the tested object detectors can be used to derive challenging and critical scenarios for the testing and type approval of automated vehicles.

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“…Consequently, the digital map is used as a strong extra sensor to raise the quality of vehicle localization performance. As the speed of ego-vehicles increases, the position estimation performance is degraded, which is the limitation or source of error for precise localization [8].…”

Section: Introductionmentioning

confidence: 99%

High Definition Map-Based Localization Using ADAS Environment Sensors for Application to Automated Driving Vehicles

Shin

Park

2020

Applied Sciences

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This paper presents high definition (HD) map-based localization using advanced driver assistance system (ADAS) environment sensors for application to automated driving vehicles. A variety of autonomous driving technologies are being developed using expensive and high-performance sensors, but limitations exist due to several practical issues. In respect of the application of autonomous driving cars in the near future, it is necessary to ensure autonomous driving performance by effectively utilizing sensors that are already installed for ADAS purposes. Additionally, the most common localization algorithm, which is usually used lane information only, has a highly unstable disadvantage in the absence of that information. Therefore, it is essential to ensure localization performance with other road features such as guardrails when there are no lane markings. In this study, we would like to propose a localization algorithm that could be implemented in the near future by using low-cost sensors and HD maps. The proposed localization algorithm consists of several sections: environment feature representation with low-cost sensors, digital map analysis and application, position correction based on map-matching, designated validation gates, and extended Kalman filter (EKF)-based localization filtering and fusion. Lane information is detected by monocular vision in front of the vehicle. A guardrail is perceived by radar by distinguishing low-speed object measurements and by accumulating several steps to extract wall features. These lane and guardrail information are able to correct the host vehicle position by using the iterative closest point (ICP) algorithm. The rigid transformation between the digital high definition map (HD map) and environment features is calculated through ICP matching. Each corrected vehicle position by map-matching is selected and merged based on EKF with double updating. The proposed algorithm was verified through simulation based on actual driving log data.

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The Correlation between Vehicle Vertical Dynamics and Deep Learning-Based Visual Target State Estimation: A Sensitivity Study

Cited by 7 publications

References 67 publications

Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence

Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence

Identification and Explanation of Challenging Conditions for Camera-Based Object Detection of Automated Vehicles

High Definition Map-Based Localization Using ADAS Environment Sensors for Application to Automated Driving Vehicles

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