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abstract: One of the important issues in the era of the Fourth Industrial Revolution is the commercialization of self driving technology. Also, core semiconductor technology and support capabilities are very important to continuously develop self driving technology. Along with looking at the status of the global semiconductor market, this paper's ultimate goal is to analyze the competitive edge of global innovative semiconductor companies' self driving solutions by dividing them into technology innovation, knowledge management, human resources, sustainable management, and commercial business factors. This paper finally provides milestones and counterplans of the Korean government, local car OEMs, and domestic semiconductor providers on future self driving solutions. For this paper's design and methodology, we extracted major factors for a competitive edge study using feedback and compelling data provided by experts and professors who have engaged in self driving solutions, as well as the automobile industry. Based on the finding of the study, the researchers found the comparative competitive edge level of global major semiconductor makers for each factor. This paper would also help the Korean government develop a strategy to cope with the self driving technology trend and global semiconductor market expansion.
author_names: Jae-Kyung Kim

Jon-Mo Yoon

Bong-Soo Lee
title: Assessment of Competitive Edge of Major Global Semiconductor Vendors for Self Driving Solutions (Level 3 and Above) Evaluation of Qualcomm, Intel, and Nvidia
year: 2020

abstract: In deep end to end learning based autonomous car design, inferencing the signal by trained model is one of the critical issues, particularly, in case of embedded component. Researchers from both academia and industry have been putting their enormous efforts in making this critical autonomous driving more reliable and safer. As research on the real car is costly and poses safety issue, we have developed a small scale, low cost, deep convolutional neural network powered self driving car model. Its learning model adopted from NVIDIA's DAVE 2 which is a real autonomous car and Kansas University's small scale DeepPicar. Similar to DAVE 2, its neural architecture uses 5 convolution layer and 3 fully connected layers with 250,000 parameters. We have considered Raspberry Pi 3B+ as the processing platform with Quad core 1.4 GHz CPU based on A53 architecture which is capable to support CNN learning model.
author_names: Arik Intisar

Md Khaled Ben Islam

Julia Rahman
title: A Deep Convolutional Neural Network Based Small Scale Test bed for Autonomous Car
venue: ICCA
year: 2020

abstract: With the growing phase of artificial intelligence and autonomous learning, the self driving car is one of the promising area of research and emerging as a center of focus for automobile industries. Behavioral cloning is the process of replicating human behavior via visuomotor policies by means of machine learning algorithms. In recent years, several deep learning based behavioral cloning approaches have been developed in the context of self driving cars specifically based on the concept of transfer learning. Concerning the same, the present paper proposes a transfer learning approach using VGG16 architecture, which is fine tuned by retraining the last block while keeping other blocks as non trainable. The performance of proposed architecture is further compared with existing NVIDIA architecture and its pruned variants (pruned by 22.2% and 33.85% using 1x1 filter to decrease the total number of parameters) Experimental results show that the VGG16 with transfer learning architecture has outperformed other discussed approaches with faster convergence.
author_names: Uppala Sumanth

Narinder Singh Punn

Sanjay Kumar Sonbhadra

Sonali Agarwal
title: Enhanced Behavioral Cloning Based self driving Car Using Transfer Learning
venue: ArXiv
year: 2020

abstract: The aviation industry has a market driven need to maintain and develop enhanced simulation capabilities for a wide range of application domains. In particular, the future growth and disruptive ability of smart cities, autonomous vehicles and in general, urban mobility, hinges on the development of state of the art simulation tools and the intelligent utilization of data. While aviation based companies have several historical and/or proprietary mission level simulation tools, there is much to learn from the current state of the art within other industries (tangential and competing in scope) as well as in academia. The purpose of this paper is to address and decompose the simulation capabilities within the key players of the autonomous vehicle and self driving car industry (Toyota, Waymo, BMW, Microsoft, NVIDIA, Uber, etc. as well as several notable startups within the high fidelity 3D mapping and simulation domain (this http URL, HERE, Cognata, etc. While providing an overview of how other companies are using simulation tools and reliable data sets, this paper will also seek to address several important and related questions, namely: the interaction between simulation and supporting tools/software, the intersection of simulation and the real world, the requirements and utilization of compute infrastructure, the appropriate levels of fidelity within simulation, and how simulation tools are critical to future safety and V&V concerns. In order for aviation based companies to adequately pursue disruptive mobility within real world environments, be it in air or on the ground, modeling and simulation tools for autonomous vehicles provide key insights into future development work and are essential technologies.
author_names: Joshua Fadaie
title: The State of Modeling, Simulation, and Data Utilization within Industry: An Autonomous Vehicles Perspective
venue: ArXiv
year: 2019

abstract: In recent years, increases in computing power have reached the automotive industry in the form of advanced driver assistance systems (ADAS) Cars that support these systems require a variety of sensors and powerful computers. Sensor data must be processed and acted upon in real time so that the car can react to changes in its environment as they occur. This is especially important when the car must take action to avoid a collision and potentially prevent loss of life. GPUs offer a large speedup for processing sensor data but their runtime performance can be unpredictable in some cases. In this paper we evaluate the predictability of GPU programs on the NVIDIA Jetson TK1 and make them perform more predictably by using Zero Copy memory.
author_names: Vance Miller
title: Determinism in GPU Programs Real Time Applications on the NVIDIA Jetson TK1
year: 2016

abstract: The use of hand gestures can be the most intuitive human machine interaction medium. The early approaches for hand gesture recognition used device based methods. These methods use mechanical or optical sensors attached to a glove or markers, which hinders the natural human machine communication. On the other hand, vision based methods are not restrictive and allow for a more spontaneous communication without the need of an intermediary between human and machine. Therefore, vision gesture recognition has been a popular area of research for the past thirty years. Hand gesture recognition finds its application in many areas, particularly the automotive industry where advanced automotive human machine interface (HMI) designers are using gesture recognition to improve driver and vehicle safety. However, technology advances go beyond active/passive safety and into convenience and comfort. In this context, one of America's big three automakers has partnered with the Centre of Pattern Analysis and Machine Intelligence (CPAMI) at the University of Waterloo to investigate expanding their product segment through machine learning to provide an increased driver convenience and comfort with the particular application of hand gesture recognition for autonomous car parking. In this thesis, we leverage the state of the art deep learning and optimization techniques to develop a vision based multiview dynamic hand gesture recognizer for self parking system. We propose a 3DCNN gesture model architecture that we train on a publicly available hand gesture database. We apply transfer learning methods to fine tune the pre trained gesture model on a custom made data, which significantly improved the proposed system performance in real world environment. We adapt the architecture of the end to end solution to expand the state of the art video classifier from a single image as input (fed by monocular camera) to a multiview 360 feed, offered by a six cameras module. Finally, we optimize the proposed solution to work on a limited resources embedded platform (Nvidia Jetson TX2) that is used by automakers for vehicle based features, without sacrificing the accuracy robustness and real time functionality of the system.
author_names: Hassene Ben Amara
title: End to End Multiview Gesture Recognition for Autonomous Car Parking System
year: 2019

abstract: Technology has become cheaper and more accessible, giving way to opportunities in technological areas that were previously inaccessible. Moreover, building a fully functional self driving vehicle has become the new gold rush in the current industries. Yet, there are many issues to solve when building an autonomous vehicle. For instance, a self driving vehicle must be reliable, consistent, predictable, and safe. There is still substantial amount of research and work that needs to be done, and more to learn, for the industry to build a truly reliable self driving vehicle. In this paper the basic concepts of self driving vehicles as well as some advanced concepts have been discussed. This project explores building a self driving car using an off the shelf remote controlled car, an NVIDIA Jetson TX2 GPU which has an ARM processor on board, infra red sensors for obstacle avoidance, and a monocular CSI camera for navigation and object detection. A combination of Google's TensorFlow, OpenCV and a Robot Operating System (ROS) enables the remote controlled car into becoming a fully self driving car.
author_names: Luis Bill

Hamid Shahnasser
title: Development and Implementation of an Autonomously Driven Vehicle Prototype
venue: 2019 IEEE 2nd International Conference on Electronics Technology (ICET)
year: 2019

abstract: Convolutional neural networks (CNN) have been proved to be an effective method in the field of artificial intelligence (AI) and large scale deploying CNN to embedded devices, no doubt, will greatly promote the development and application of AI into the practical industry. However, mainly due to the space time complexity of CNN, computing power, memory bandwidth and flexibility are performance bottlenecks. In this paper, a framework containing model compression and hardware acceleration is proposed to solve the above problems. This framework consists of a mixed pruning method, data storage optimization for efficient memory utilization and an accelerator for mapping CNN on field programmable gate array (FPGA) The mixed pruning method is used to compress the model, and data bit width is reduced to 8 bit by data quantization. Accelerator based on FPGA makes it flexible, configurable and efficient for CNN implementation. The model compression is evaluated on NVIDIA RTX2080Ti, and the results illustrate that the VGG16 is compressed by <inline formula> <tex math notation="LaTeX"$30\times /tex math>/inline formula> and the fully convolutional network (FCN) is compressed by <inline formula> <tex math notation="LaTeX"$11\times /tex math>/inline formula> within 1% accuracy loss. The compressed model is deployed and accelerated on ZCU102, which is up to <inline formula> <tex math notation="LaTeX"$1.7\times /tex math>/inline formula> and <inline formula> <tex math notation="LaTeX"$24.5\times /tex math>/inline formula> better in energy efficiency compared with RTX2080Ti and Intel i7 7700.
author_names: Xuepeng Chang

Huihui Pan

Weiyang Lin

Huijun Gao
title: A Mixed Pruning Based Framework for Embedded Convolutional Neural Network Acceleration
venue: IEEE Transactions on Circuits and Systems I: Regular Papers
year: 2021

abstract: The vision of autonomous vehicles on our roads is likely to become a reality in the near future. However, many challenges need to be explored and resolved before self driving cars are feasible. NetApp, a leading company in the storage industry, is working with NVIDIA, a leading AI computational company, to build the first industrial reference architecture for the automotive industry (Arnette Lin, 2020) This white paper introduces the reference architecture proposed by NetApp and NVIDIA and describes the challenges and concerns for building and validating this architecture. It also discusses performance considerations to help new AI teams and their IT colleagues accelerate their research, engineering workflows, and processes. In partnership with
author_names: Sung-Han Lin
title: Training Workflows for Faster Self Driving Results
year: 2020

abstract: Abstract To survive in an intensively competitive environment, semiconductor companies need to be more agile, responsive, and flexible than before. Generally, semiconductor industry consists of three business models: integrated design manufacturer, fabless chip design, and foundry business. In general, semiconductor firms are affected by three drivers: new entrants and rivals (competition) main customers (demand) and process technologies (R&D) Inspired by Michael Porter's five force analysis, a novel framework is presented to accomplish the following goals: (1) The interactions between the top three foundry firms, TSMC, Samsung, and Global Foundries, are analyzed to reveal managerial insights, (2) The main customers of TSMC including Apple, Huawei, Qualcomm, Mediatek, AMD, and NVidia are incorporated to into sales forecasting, and (3) Technological diffusion across the mature process (above 70 nm) the medium process (between 20 nm and 70 nm) and the advanced process (below 20 nm) is captured to predict sales revenues. Key findings are shown as follows: (1) a large scale foundry frim benefits from the existence of a small scale firm, (2) the inclusion of main customers significantly improves the performance of sales forecasting, (3) the advanced process gradually benefits from the mature process while it rapidly replaces the medium process in a "predator prey" way.
author_names: Chih-Hsuan Wang

Hsuan-Chen Lin
title: Competitive substitution and technological diffusion for semiconductor foundry firms
venue: Adv. Eng. Informatics
year: 2021