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11.9563 TL h This paper describes the moving vehicle detection and tracking module that we developed for our autonomous driving robot Junior. /Font <<
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Vision-based perception using deep learning reports state-of-the-art accuracy, but the performance is susceptible to variations in the environment. 105.816 14.996 l
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0 g /F2 89 0 R The experimental results show that the proposed network is better than baseline algorithms in varying environmental conditions.Object Detection and Tracking are at the heart of the Computer Vision field; and Deep Learning through Neural Networks and Convolutional Neural Networks have proven to be the most efficient and performant methods to go about these tasks in the recent years especially in emerging and leading industries such as autonomous vehicle and automotive driving assistance. /R84 109 0 R /F1 159 0 R
/R93 122 0 R [ (RPN) -347.991 (module) -347.991 (\050Sect\056) -348.988 (3\0561\051) -348.011 (which) -348.006 (outputs) -348.02 (corresponding) -349.005 (left) ] TJ In the track hypothesis generation step, each segment is associated with an existing track maintained over multiple scans. The radar features are formulated using a novel feature descriptor, termed as the "sparse radar image". BT T* The RVNet input branches contain separate branches for the monocular camera and the radar features.
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/ExtGState << In this paper, we propose a radar and vision-based deep learning perception framework termed as the SO-Net to address the limitations of vision-based perception. T*
1 1 1 rg 79.008 23.121 78.16 23.332 77.262 23.332 c 10 0 0 10 0 0 cm Multiple Sensor Fusion for Detection, Classification and Tracking of Moving Objects in Driving Environments. Simulation results are presented for two heavily interfering targets; these illustrate the dramatic improvements obtained by computing joint probabilities.This paper presents a new approach to the problem of tracking when the source of the measurement data is uncertain.
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[ (tonomous) -299.007 (driving) -299.018 (by) -299.988 (fully) -298.987 (e) 19.9918 (xploiting) -299.004 (the) -299.002 (spar) 9.98118 (se) -299.006 (and) -300.014 (dense) 10.0057 (\054) ] TJ Furthermore, we introduce approximations to handle the inherently complex data association problem. In contrast to the Kalman filter, it does not require data association in time and space. /R12 22 0 R
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Extended Object Tracking: Introduction, Overview and Applications Karl Granstr¨om, Marcus Baum, and Stephan Reuter Abstract—This article provides an elaborate overview of current research in extended object tracking.
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The SO-Net input branches correspond to vision and radar feature extraction branches. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs.
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/R115 144 0 R 11.9551 TL --BOOK JACKET.We present and evaluate two variants of an algorithm for simultaneously segmenting and modeling a mixed-density unstructured 3D point cloud by ellipsoidal (Gaussian) region growing. >> T* << 5 0 obj
/R7 16 0 R T* For evaluation purposes, a highly accurate reference trajectory has been recorded using an RTK-supported DGPS receiver. T*
endobj first-order accuracy. >> Robotics [cs.RO].
Q We extend the use of the UKF to a broader class of T*
/R10 17 0 R /R10 17 0 R << /Parent 1 0 R >> endobj 35.125 TL
/R8 32 0 R /Count 9 The radar features are formulated using a novel feature descriptor, termed as the “sparse radar image”.
Radar sensors are well suited for this task due to their robustness to environmental influences and direct measurement of the radial (Doppler) velocity. 82.684 15.016 l >> /MediaBox [ 0 0 612 792 ] >>
It is assumed that one object of interest (‘target’) is in track and a number of undesired returns are detected and resolved at a certain time in the neighbourhood of the predicted location of the target's return. Our surround radar perception is able to track these objects 360 degrees around the vehicle, even as they pass between multiple radars. /R8 32 0 R
mean and covariance of the transformed GRV, which may lead to /Type /Catalog [ (consider) -340.985 (3D) -340.995 (object) -340.007 (localization) -341.007 (as) -341.002 (a) -341.007 (learning\055aided) -341.017 (geom\055) ] TJ
In comparison to related methods it is not based on temporal filtering, e.g. /Resources <<
A suboptimal estimation procedure that takes into account all the measurements that might have originated from the object in track but does not have growing memory and computational requirements is presented.
88.993 4.33906 Td [ (to) -376.003 (the) -376.008 (right) -375.994 (image) -374.982 (according) -376.006 (to) -376.006 (their) -376.006 (depth) -375.996 (relations) -375.991 (with) ] TJ /R49 52 0 R It explains state estimator design using a balanced combination of linear systems, probability, and statistics."
Network", Cuvillier Verlag, 2005.Data Association, Encyclopedia of Systems and ControlY.
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