自動運転車
TOPIC

GTC Japan 2018 で AI 自動車のエキスパートと交流する

GTC Japan 2018 では、自動車メーカー、1 次サプライヤー、ロボタクシー開発者、研究者など、輸送業界のエキスパートが一堂に会し、GPU テクノロジと人工知能が業界全体を変える仕組みを紹介します。GPU ベースのスーパーコンピューティングがディープラーニング、自然言語処理、ジェスチャーコントロールの革新技術を可能にする仕組みをご覧ください。スーパーコンピューティングは自動運転のパワーとなり、運転のあり方を変えています。詳しくは GTC Japan 2018 に是非ご参加ください。

注目のスピーカー

菅沼直樹

金沢大学
新学術創成研究機構未来社会創造研究コア自動運転ユニットユニットリーダー准教授

市街地の自動運転における AI の果たす役割と未来

馬路徹

エヌビディア合同会社
技術顧問、GPU エバンジェリスト

自動運転車からサポート・クラウドまでのトータルシステム

田中道昭

立教大学ビジネススクール
教授

「AI × 自動運転」のビジョン・テクノロジー・戦略

その他のセッションを探す

ハンズオントレーニングにご参加ください

NVIDIA Deep Learning Institute が提供するディープラーニングのトレーニング、デプロイ、アクセラレーテッドコンピューティングのハンズオンラボにご参加ください。

詳細はこちら

関連コンテンツ

ディープラーニング/AI

自律動作マシン

開発者ツール

仮想現実/拡張現実

HPC/スーパーコンピューティング

ADVANCED AI METHODS FOR AUTONOMOUS VEHICLE SIMULATION

DANNY ATSMON

CEO
Cognata

Advanced AI Methods for Autonomous Vehicle Simulation

Realistic automotive simulation platforms, where virtual cars operate in remarkably true-to-life conditions, will be a vital part of developing and testing autonomous vehicles. The technology behind the Cognata simulation engine is heavily based on deep learning, computer vision, and other advanced AI methods. We'll discuss our cloud-based simulation engine, explore how it works and explain how to develop with it.

ABOUT THE SPEAKER: Danny Atsmon, an expert in advanced driver-assistance systems (ADAS) and deep learning, is the CEO at Cognata Ltd. He has been in the business of launching high-tech products for more than 20 years. Before joining Cognata, Danny served as HARMAN's (now Samsung) director of ADAS and senior director of machine learning. He co-founded two startups, Picitup and iOnRoad, the latter of which uses a phone's native camera to detect vehicles in front of a car. Danny also holds several patents and has won many top industry awards, including the CES award, CTIA award, Microsoft Think Next, and Qualcomm QPrize. Danny has a Bachelor of Science in physics from Tel Aviv University, and is a graduate of the Israeli Defense Forces Haman Talpiot program, where he served in the elite Unit 8200.

LEX FRIDMAN

RESEARCH SCIENTIST
MIT

The Future of the In-Car Experience

As the race to full autonomy accelerates, the in-cab transportation experience is also being redefined. Future vehicles will sense the passengers' identities and activities, as well as their cognitive and emotional states, to adapt and optimize their experience. AI capable of interpreting what we call “people analytics” — captured through their facial and vocal expressions, and aspects of the context that surrounds them — will power these advances. In this talk, we will give an overview of our Emotion AI solution, and describe how we employ techniques like deep learning based spatio-temporal modeling. By combining these techniques with a large-scale dataset, we can develop AI capable of redefining the in-car experience.

ABOUT THE SPEAKER: Lex Fridman is a research scientist at Massachusetts Institute of Technology, working on computer vision and deep learning approaches in the context of semi-autonomous vehicles and human-centered artificial intelligence systems. His work focuses on learning-based methods that leverage large-scale, real-world data. Previously, Lex worked on deep learning methods for large-scale behavior-based authentication at Google. He received his Bachelor of Science, Master of Science, and Ph.D. from Drexel University, where he worked on applications of machine learning, computer vision, and decision fusion techniques in a number of fields such as robotics, active authentication, and activity recognition.

SOLVING REAL-TIME PERCEPTION: THE MOST DIFFICULT PROBLEM IN AUTONOMOUS DRIVING

FORREST IANDOLA

CEO
DeepScale

Solving Real-Time Perception: The Most Difficult Problem in Autonomous Driving

This talk will show how deep neural networks can ingest raw data from multiple types of sensors to generate improved perception results in real-time, using processors fit for automotive mass production. Today's mass-produced driver-assistance systems are typically implemented with a late-fusion paradigm. This approach has a number of limitations in terms of accuracy, portability, and robustness to sensor failure. In this talk, we propose an earlier stage of fusion, called Deep Sensor Fusion, where sensors transmit raw data over higher bandwidth in-vehicle networking, which is already used in mass production today.

ABOUT THE SPEAKER: Forrest Iandola completed a PhD in Electrical Engineering and Computer Science at UC Berkeley, where his research focused on deep neural networks. His advances in scalable training and efficient implementation of deep neural networks led to the founding of DeepScale, where he is CEO. DeepScale is focused entirely on building perception and sensor fusion systems for autonomous vehicles, and DeepScale has a number of engagements with automakers and automotive suppliers.

KAL MOS

MERCEDES-BENZ, Vice President Artificial Intelligence and Digital Vehicle

ABOUT THE SPEAKER: Dr. Kal Mos is the Vice President of Artificial Intelligence and Digital Vehicle at Mercedes-Benz Research & Development North America. In his role, Kal is responsible for innovations in areas such as Machine Learning, Connected Services, User Experience, Big Data and Analytics.

Kal joined Mercedes-Benz in 2002 to create and lead the Connected Infotainment and Consumer Electronics Integration group. Since then, he has been turning visionary ideas into exceptional products such as the Google Home and Amazon Alexa integration into all new Mercedes-Benz models in the U.S. He was significantly involved in the development of the User Experience of the self-driving research car F 015, as well as the 3D renderings in real time of the Concept IAA show car. Recently, the team launched MBUX, Mercedes-Benz User Experience, the latest and greatest infotainment system, which makes the vehicle smarter and more personalized than ever before – available in the new Mercedes-Benz A-Class.

Prior to Mercedes-Benz, Kal worked as the Chief Technologist of OnStar where he led a team of General Motors¹ Telematics experts and engineers in developing OnStar in-vehicle hardware and software platform.

Earlier in his career, Kal held several management positions building and leading high-performance engineering teams that developed software applications for Consumer Electronics Devices and Cloud Services in Switzerland, Japan, India and US.

Kal is the author of several publications and holds three patents. He has a Ph.D. in Engineering from the University of Tokyo in Japan.

MELISSA SMITH

CLEMSON UNIVERSITY, Associate Professor

Deep Learning for Automated Systems: From the Warehouse to the Road

ABOUT THE SPEAKER: Melissa C. Smith received her B.Sc. (1993) and M.Sc. (1994) in electrical engineering from Florida State University and a Ph.D. (2003) from the University of Tennessee. Melissa was a research associate at the Oak Ridge National Laboratory from 1994 to 2006, where she designed ASICs and FPGA-based systems for high-speed signal-processing applications. She joined the ECE faculty at Clemson in 2006, where her current research focuses on the performance analysis and optimization of emerging heterogeneous computing architectures for application domains, including machine learning, high-performance or real-time embedded applications, and medical and image processing. She has been a senior member of IEEE since 2005 and received the YMCA Stratford Award for Diversity and Inclusion in 2017, Clemson National Scholars Program Award of Distinction in 2009, and Tribute to Women Award in Science and Technology from the YWCA in 2006.

自動運転車
TOPIC

GTC Japan 2018 で AI 自動車のエキスパートと交流する