2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) 2020
DOI: 10.1109/iemcon51383.2020.9284930
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Using a Combination of LiDAR, RADAR, and Image Data for 3D Object Detection in Autonomous Vehicles

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Cited by 6 publications
(6 citation statements)
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“…Reference [ 25 ] combined Content-Based Filtering (CBF) and Network-based Collaborative Filtering (NCF), and proposed a deep-learning-based collaborative filtering method DeepCCF for personalized resource recommendation. Reference [ 26 ] proposed a deep neural model based on transfer learning, which integrated cross-domain knowledge to achieve more accurate POI recommendations. Using deep transfer learning, this method learned the complex user-project interaction relationship and more accurately captured the overall preferences of users for transferring.…”
Section: Related Workmentioning
confidence: 99%
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“…Reference [ 25 ] combined Content-Based Filtering (CBF) and Network-based Collaborative Filtering (NCF), and proposed a deep-learning-based collaborative filtering method DeepCCF for personalized resource recommendation. Reference [ 26 ] proposed a deep neural model based on transfer learning, which integrated cross-domain knowledge to achieve more accurate POI recommendations. Using deep transfer learning, this method learned the complex user-project interaction relationship and more accurately captured the overall preferences of users for transferring.…”
Section: Related Workmentioning
confidence: 99%
“…In order to prove the advantages of the proposed personalized POI recommendation method, under the same experimental conditions, the recommended method in reference [ 26 , 27 ] was compared with the proposed method. Precision @ k , recall @ k, and F 1@ k are used to measure the recommendation effect of each model and set the recommended number K of POI to 5, 10, and 15, respectively, to exclude the contingency of the experimental results.…”
Section: Experiments and Analysismentioning
confidence: 99%
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“…With a wide range of applications of CNNs in image classification and detection. They can be implemented on an Electronic Control Unit (ECU) for autonomous driving assistance in an automobile application [ 7 , 8 , 9 , 10 , 11 ]. For instance, the authors in [ 10 ] discuss the application of 3D image detection for an autonomous vehicle.…”
Section: Introductionmentioning
confidence: 99%
“…They can be implemented on an Electronic Control Unit (ECU) for autonomous driving assistance in an automobile application [ 7 , 8 , 9 , 10 , 11 ]. For instance, the authors in [ 10 ] discuss the application of 3D image detection for an autonomous vehicle. This 3D image analysis can be handled through CNNs by applying multiple filters on the same frame for different feature extraction.…”
Section: Introductionmentioning
confidence: 99%