Using a Combination of LiDAR, RADAR, and Image Data for 3D Object Detection in Autonomous Vehicles

Sahba, Ramin; Sahba, Amin; Sahba, Farshid

doi:10.1109/iemcon51383.2020.9284930

Cited by 6 publications

(6 citation statements)

References 22 publications

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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%

“…The experimental results are shown in Figure 3 , Figure 4 and Figure 5 . It should be noted that methods of “Transfer learning in [ 26 ]” and “LSTM-Seq2Seq in [ 27 ]” are compared in this part with the same characteristics mentioned in their original papers. For more explanations and details, please refer to these references.…”

Section: Experiments and Analysismentioning

confidence: 99%

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Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data

Chang

Sun

2023

Sensors

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Aiming at the problem that the existing Point of Interest (POI) recommendation model in social network big data is difficult to extract deep feature information, a POI recommendation model based on deep learning in social networks and big data is proposed in this article. The input data are all gathered through intelligent sensors to apply some raw data pre-processing tasks and thus reduce the computational burden on the model. First, a POI static feature extraction method based on symmetric matrix decomposition is designed to capture the geographical location and POI category features in Location-Based Social Networking (LBSN). Second, the improved Continuous Bags-of-Words (CBOW) model is used to extract the semantic features in the user comment information, and realize the implicit vector representation of POI in geographic, category, semantic and temporal feature space. Finally, by adaptively selecting relevant check-in activities from the check-in history to learn and change user preferences, the Geographical-Spatiotemporal Gated Recurrent Unit Network (GSGRUN) can distinguish the user preferences of different check-in. Experiments show that when the length of the recommendation list is 15, the precision of the proposed algorithm on the loc-Gowalla data set is 0.0686, the recall is 0.0769, and the precision on the loc-Brightkite data set is 0.0659, the recall is 0.0835, both of which are better than the comparative recommendation methods. Therefore, compared with the comparison methods, the proposed method can significantly improve the performance of the POI recommendation system.

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Section: Related Workmentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

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Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data

Chang

Sun

2023

Sensors

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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%

Parameterizable Design on Convolutional Neural Networks Using Chisel Hardware Construction Language

2023

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This paper presents a parameterizable design generator on convolutional neural networks (CNNs) using the Chisel hardware construction language (HCL). By parameterizing structural designs such as the streaming width, pooling layer type, and floating point precision, multiple register–transfer level (RTL) implementations can be created to meet various accuracy and hardware cost requirements. The evaluation is based on generated RTL designs including 16-bit, 32-bit, 64-bit, and 128-bit implementations on field-programmable gate arrays (FPGAs). The experimental results show that the 32-bit design achieves optimal hardware performance when setting the same weights for estimating the quality of the results, FPGA slice count, and power dissipation. Although the focus is on CNNs, the approach can be extended to other neural network models for efficient RTL design.

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6G IoV Networks Driven by RF Digital Twin Modeling

Liu,

Sun,

Marine

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Using a Combination of LiDAR, RADAR, and Image Data for 3D Object Detection in Autonomous Vehicles

Cited by 6 publications

References 22 publications

Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data

Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data

Parameterizable Design on Convolutional Neural Networks Using Chisel Hardware Construction Language

6G IoV Networks Driven by RF Digital Twin Modeling

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