標(biāo)題: Titlebook: Autonomous driving algorithms and Its IC Design; Jianfeng Ren,Dong Xia Book 2023 Publishing House of Electronics Industry 2023 Autonomous [打印本頁] 作者: 去是公開 時間: 2025-3-21 16:05
書目名稱Autonomous driving algorithms and Its IC Design影響因子(影響力)
書目名稱Autonomous driving algorithms and Its IC Design影響因子(影響力)學(xué)科排名
書目名稱Autonomous driving algorithms and Its IC Design網(wǎng)絡(luò)公開度
書目名稱Autonomous driving algorithms and Its IC Design網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Autonomous driving algorithms and Its IC Design被引頻次
書目名稱Autonomous driving algorithms and Its IC Design被引頻次學(xué)科排名
書目名稱Autonomous driving algorithms and Its IC Design年度引用
書目名稱Autonomous driving algorithms and Its IC Design年度引用學(xué)科排名
書目名稱Autonomous driving algorithms and Its IC Design讀者反饋
書目名稱Autonomous driving algorithms and Its IC Design讀者反饋學(xué)科排名
作者: MENT 時間: 2025-3-21 23:29
3D Object Detection,iving in Sects. . and ., respectively. Based on the existing work on sensor modalities, the discussion will be summarized into single sensor-based methods, point cloud-based methods and fusion methods. In Sect. ., a paper is selected and combined with open-source code to discuss 3D object detection.作者: Lymphocyte 時間: 2025-3-22 01:04 作者: tenuous 時間: 2025-3-22 08:33 作者: apropos 時間: 2025-3-22 12:12 作者: Veneer 時間: 2025-3-22 15:38 作者: 采納 時間: 2025-3-22 17:10
Autonomous Driving ASICs,ort the development of autonomous vehicles around this trend. This chapter focuses on reviewing how the industry is currently meeting the challenges of semiconductor technology innovation in this field.作者: 懶洋洋 時間: 2025-3-22 21:14
Deep Learning Model Optimization,nity and has made great strides in the past few years..This chapter divides these methods into four categories: (1) parameter pruning and sharing; (2) low-rank factorization; (3) transfer/compact convolutional filters; and (4) knowledge distillation. For parameter pruning- and sharing-based solution作者: visceral-fat 時間: 2025-3-23 03:52 作者: 表示向前 時間: 2025-3-23 05:32 作者: 完全 時間: 2025-3-23 11:04 作者: Celiac-Plexus 時間: 2025-3-23 14:28
Autonomous Driving Software Architecture,t of information from the cars, making the right decisions, and building the software algorithms. This processing requires massive software implementation. There is one trend, both in industry and academia, to consolidate clusters of more powerful application processors and accelerators into higher-作者: Pastry 時間: 2025-3-23 21:39 作者: reptile 時間: 2025-3-23 23:25
Book 2023years. The implementation of autonomous driving requires new sources of sensory data, such as cameras, radars, and lidars, and the algorithm processing requires a high degree of parallel computing. In this regard, traditional CPUs have insufficient computing power, while DSPs are good at image proce作者: Psychogenic 時間: 2025-3-24 03:21
https://doi.org/10.1007/978-1-84882-981-7ranteed within a few kilometers. The second question, regardless of the conditions, needs to have a map sufficient for the positioning task. In this chapter, we mainly see how SLAM is being applied to autonomous driving and then we focus on the high-definition map creation.作者: 有其法作用 時間: 2025-3-24 08:49
Laplacian Eigenvalues of Threshold Graphs,solutions, a special structured convolution filter is designed to reduce the parameter space and save storage/computation. Knowledge distillation methods learn distillation models and train more compact neural networks to reproduce the output of larger networks.作者: 歡笑 時間: 2025-3-24 11:37
Laplacian Eigenvalues of Threshold Graphs,fety features should be incorporated into the whole software design pipeline and introduce some tools from commercial companies to help the software design. Finally, at the end of this chapter, we would like to show one example of software architecture from academia.作者: 口味 時間: 2025-3-24 18:46 作者: ADAGE 時間: 2025-3-24 20:08 作者: 脫落 時間: 2025-3-25 00:06 作者: Aprope 時間: 2025-3-25 06:00
Positive Definite Completion Problem,ision solutions and mainly focus on deep learning-based solutions for lane detection. Additionally, we also present a one-lane detection evaluation system, including offline and online systems. Finally, we use one lane detection algorithm and code to show how lane detection works in an autonomous driving system.作者: 壯麗的去 時間: 2025-3-25 11:00 作者: 飛鏢 時間: 2025-3-25 12:20
Connectedness, Trees, and Hypergraphs,rtation system (C-ITS) that reduces congestion and pollution while making travel faster and more efficient..This chapter introduces how C-V2X will change autonomous driving, how C-V2X works, and C-V2X deployment by different countries.作者: Connotation 時間: 2025-3-25 15:52 作者: seduce 時間: 2025-3-25 21:08
Autonomous Driving SoC Design,gn is needed, an FPGA or ASIC can still provide the best power/performance results. This is why an increasing number of design teams are choosing to implement their algorithms. In this chapter, we take Texas Soc as one example to show that we can design the automotive SoC to meet the requirements.作者: 隼鷹 時間: 2025-3-26 02:35
Introduction to 5G C-V2X,rtation system (C-ITS) that reduces congestion and pollution while making travel faster and more efficient..This chapter introduces how C-V2X will change autonomous driving, how C-V2X works, and C-V2X deployment by different countries.作者: 去世 時間: 2025-3-26 06:40
https://doi.org/10.1007/978-1-84882-981-7 planning and control algorithms, especially for urban environments, and discuss their effectiveness. The discussion of planning control in this chapter provides insight into the strengths and limitations of various approaches and helps in choosing a system-level design.作者: cardiac-arrest 時間: 2025-3-26 08:29 作者: 平息 時間: 2025-3-26 14:42
riving products and services and potential business opportun.With the rapid development of artificial intelligence and the emergence of various new sensors, autonomous driving has grown in popularity in recent years. The implementation of autonomous driving requires new sources of sensory data, such作者: liaison 時間: 2025-3-26 17:48
https://doi.org/10.1007/978-1-84882-981-7hods, point cloud-based methods and fusion methods. In Sect. ., a paper is selected and combined with open-source code to discuss 3D object detection. Finally, in Sect. ., current research challenges and future research directions are discussed as follows:作者: HAUNT 時間: 2025-3-26 21:59 作者: 巫婆 時間: 2025-3-27 03:48 作者: SPALL 時間: 2025-3-27 05:48 作者: 察覺 時間: 2025-3-27 12:42
https://doi.org/10.1007/978-981-99-2897-2Autonomous driving; SLAM; software/hardware codesgin; chip design; deep learning作者: Gossamer 時間: 2025-3-27 17:32
978-981-99-2899-6Publishing House of Electronics Industry 2023作者: 陳腐的人 時間: 2025-3-27 20:39
Jianfeng Ren,Dong XiaSystematically introduces readers to all aspects of autonomous driving algorithm, software and chip design.Highlights potential autonomous driving products and services and potential business opportun作者: 符合規(guī)定 時間: 2025-3-27 23:06 作者: 死亡率 時間: 2025-3-28 04:49 作者: Decline 時間: 2025-3-28 06:28
https://doi.org/10.1007/978-1-84882-981-7iving in Sects. . and ., respectively. Based on the existing work on sensor modalities, the discussion will be summarized into single sensor-based methods, point cloud-based methods and fusion methods. In Sect. ., a paper is selected and combined with open-source code to discuss 3D object detection.作者: BLUSH 時間: 2025-3-28 13:24
Positive Definite Completion Problem, be used to solve the problem of lane detection in a more efficient way. However, the key challenge for lane detection systems is to adapt to the demands of high reliability and diverse road conditions. An efficient way to construct a robust and accurate advanced lane detection system is to fuse mul作者: shrill 時間: 2025-3-28 15:07 作者: ABOUT 時間: 2025-3-28 21:24
https://doi.org/10.1007/978-1-84882-981-7f self-driving cars. However, many problems make it impossible for SLAM algorithms to drive vehicles for hundreds of kilometers under very different conditions. There are two main problems dealing with SLAM for self-driving cars: (1) localization drifts over time and (2) maps may not necessarily sat作者: Nonflammable 時間: 2025-3-29 01:35 作者: Lucubrate 時間: 2025-3-29 04:41
Positive Definite Completion Problem,ort the development of autonomous vehicles around this trend. This chapter focuses on reviewing how the industry is currently meeting the challenges of semiconductor technology innovation in this field.作者: Devastate 時間: 2025-3-29 10:52
Laplacian Eigenvalues of Threshold Graphs,nity and has made great strides in the past few years..This chapter divides these methods into four categories: (1) parameter pruning and sharing; (2) low-rank factorization; (3) transfer/compact convolutional filters; and (4) knowledge distillation. For parameter pruning- and sharing-based solution作者: 五行打油詩 時間: 2025-3-29 11:43
https://doi.org/10.1007/978-1-84882-981-7n intervention, however, it requires a complex sensor framework that captures not only vehicle data but also data from its surroundings. These sensors include LiDAR, radar, video, cameras, and more, which continuously generate massive amounts of data about the environment around the car in real time作者: 危機(jī) 時間: 2025-3-29 19:27
https://doi.org/10.1007/978-1-84882-981-7re in a CPU, DSP, or GPU, but these algorithms are expensive and may have other drawbacks. CPUs cannot be fast or efficient enough, while DSPs are good at image processing but lack sufficient performance for deep AI. While GPUs are good at training, they are too power hungry. Another option for in-v作者: 構(gòu)想 時間: 2025-3-29 21:34
Laplacian Eigenvalues of Threshold Graphs,ngineering, and the architectures used to build autonomous driving systems. The development of autonomous driving systems involves domain-specific algorithms, architectures, systems engineering, and technical measures. In this chapter, we mainly introduce some open source operating systems and comme作者: 粗野 時間: 2025-3-29 23:55
Laplacian Eigenvalues of Threshold Graphs,t of information from the cars, making the right decisions, and building the software algorithms. This processing requires massive software implementation. There is one trend, both in industry and academia, to consolidate clusters of more powerful application processors and accelerators into higher-作者: 偽證 時間: 2025-3-30 07:28
Connectedness, Trees, and Hypergraphs,ability and economies of scale. In June 2017, 3GPP introduced cellular vehicle-to-everything (C-V2X) technology in Release 14 of its standard, and the technology was further developed with Release 15, which was completed in June 2019. This cellular technology is made possible by leveraging LTE and 5作者: 著名 時間: 2025-3-30 11:19 作者: Immunoglobulin 時間: 2025-3-30 12:37 作者: Jejune 時間: 2025-3-30 20:07
Positive Definite Completion Problem,ort the development of autonomous vehicles around this trend. This chapter focuses on reviewing how the industry is currently meeting the challenges of semiconductor technology innovation in this field.作者: Apoptosis 時間: 2025-3-31 00:20 作者: 表被動 時間: 2025-3-31 02:05
Challenges of Autonomous Driving Systems, while designing autonomous driving systems from the perspectives of ., .s, . and .. Then, we also briefly discussed several key components to design one autonomous driving system, such as the perception system, decision making and vehicle control. In this chapter, we also described several availabl作者: Legion 時間: 2025-3-31 05:24 作者: 到婚嫁年齡 時間: 2025-3-31 09:21
Laplacian Eigenvalues of Threshold Graphs, while designing autonomous driving systems from the perspectives of ., .s, . and .. Then, we also briefly discussed several key components to design one autonomous driving system, such as the perception system, decision making and vehicle control. In this chapter, we also described several availabl
