Mohammad Javad Shafiee, Mirko Nentwig, Yohannes Kassahun, Francis Li, Stanislav Bochkarev, Akif Kamal, David Dolson, Secil Altintas, Arif Virani, Alexander Wong
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7 pages; BMVC Workshop on Visual AI and Entrepreneurship (VAIE)
An effective deep learning development process is critical for widespread industrial adoption, particularly in the automotive sector. A typical industrial deep learning development cycle involves customizing and re-designing an off-the-shelf network architecture to meet the operational requirements of the target application, leading to considerable trial and error work by a machine learning practitioner. This approach greatly impedes development with a long turnaround time and the unsatisfactory quality of the created models. As a result, a development platform that can aid engineers in greatly accelerating the design and production of compact, optimized deep neural networks is highly desirable. In this joint industrial case study, we study the efficacy of the GenSynth AI-assisted AI design platform for accelerating the design of custom, optimized deep neural networks for autonomous driving through human-machine collaborative design. We perform a quantitative examination by evaluating 10 different compact deep neural networks produced by GenSynth for the purpose of object detection via a NASNet-based user network prototype design, targeted at a low-cost GPU-based accelerated embedded system. Furthermore, we quantitatively assess the talent hours and GPU processing hours used by the GenSynth process and three other approaches based on the typical industrial development process. In addition, we quantify the annual cloud cost savings for comprehensive testing using networks produced by GenSynth. Finally, we assess the usability and merits of the GenSynth process through user feedback. The findings of this case study showed that GenSynth is easy to use and can be effective at accelerating the design and production of compact, customized deep neural network.
Collaborative Perception Datasets for Autonomous Driving: A Review
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing)（中国矿业大学（北京）机械与电子工程学院）；State Key Laboratory of Robotics and System, Harbin Institute of Technology（哈尔滨工业大学机器人系统国家重点实验室）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与移动系统国家重点实验室）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；School of Mechatronics Engineering, Harbin Institute of Technology（哈尔滨工业大学机械电子工程学院）；Department of Electronic & Electrical Engineering, University of Bath（巴斯大学电子与电气工程系）
School of Information, Renmin University of China（中国人民大学信息学院）；Sony Research and Development Center China（索尼（中国）研发有限公司）；National University of Defense Technology（国防科技大学）
Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey
State Key Laboratory of Robotics and System（机器人系统国家重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）；State Key Laboratory of Intelligent Green Vehicle and Mobility（智能绿色车辆与移动性国家重点实验室）；Tsinghua University（清华大学）；the School of Mechanical and Aerospace Engineering（机械与航空航天工程学院）；Nanyang Technological University（南洋理工大学）；Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence（北京交通数据挖掘与具身智能重点实验室）；Beijing Jiaotong University（北京交通大学）；the Institute for Infocomm Research（信息通信研究所）；A*STAR；the Engineering Cluster（工程集群）；the Singapore Institute of Technology（新加坡理工学院）
Toyota TRACE-Zurich team at the Computer Vision Lab, ETH Zurich（苏黎世联邦理工学院（ETH Zurich）计算机视觉实验室丰田TRACE苏黎世团队）；Toyota TRACE-Leuven team at the Dept of Electrical Engineering ESAT, KU Leuven（鲁汶大学（KU Leuven）电气工程系ESAT丰田TRACE鲁汶团队）
Unsupervised Collaborative Domain Adaptation for Driving Scene Parsing
College of Electronic and Information Engineering, Tongji University（同济大学电子与信息学院）；Department of Control Science and Engineering, Harbin Institute of Technology（控制科学与工程系，哈尔滨工业大学）；Department of Vehicle Control System and Software Development, NIO（车辆控制系统与软件开发部，蔚来汽车）；School of Computer Science and Technology, Tongji University（计算机科学与技术学院，同济大学）；Key Laboratory of Embedded System and Service Computing (Ministry of Education), Tongji University（嵌入式系统与服务计算重点实验室（教育部），同济大学）
MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction
Chongqing University of Posts and Telecommunications（重庆邮电大学）；College of Computer and Information Science, Chongqing Normal University（重庆师范大学计算机与信息科学学院）
AGC-Drive: A Large-Scale Dataset for Real-World Aerial-Ground Collaboration in Driving Scenarios
University of Science and Technology Beijing（北京科技大学）；Xiamen NEVC Advanced Electric Powertrain Technology Innovation Center（厦门NEVC先进电驱技术创新中心）；Shanghai Jiao Tong University（上海交通大学）
