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Abstract:
Automated driving systems are challenging to bring to market due to an enormous number of scenarios and environment parameter combinations that must be validated. Using current real-world tests for human-driven vehicles on newly developed automated driving technology is no longer feasible. This session will explore how testing in the virtual world enables manufacturers and regulators to validate a wide variety of traffic situations without hazard. Especially when using deep learning algorithms for automated driving functions, a scalable, powerful, and consistent toolchain is required. To homologate automated vehicles with confidence, simulation platforms will be crucial.
Automated driving systems are challenging to bring to market due to an enormous number of scenarios and environment parameter combinations that must be validated. Using current real-world tests for human-driven vehicles on newly developed automated driving technology is no longer feasible. This session will explore how testing in the virtual world enables manufacturers and regulators to validate a wide variety of traffic situations without hazard. Especially when using deep learning algorithms for automated driving functions, a scalable, powerful, and consistent toolchain is required. To homologate automated vehicles with confidence, simulation platforms will be crucial.  Back
 
Topics:
Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9804
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Abstract:

Digital Homologation with Simulation for R&D and validation using Nvidia GPU in cloud and HIL. Securing and homologating automated driving functions presents a huge challenge for market introduction due to an enormous number of scenarios and environment parameter combinations. Confronting conventional real world tests with the new challenges of automated driving is not feasible anymore, and yields to a virtualisation of the testing methods by means of X-in-the-Loop approaches. Especially when using Deep Learning Algorithms for automated driving functions, a scalable, powerful and consistent toolchain is required. Together with TÃV SÃD and the University of Applied Sciences in Kempten, AVL is working on such a consistent toolchain. Different configurations for the development and also approaches for the homologation shall be introduced. Especially the challenge of close-loop-testing, including the vehicle and powertrain dynamics, should be addressed. The challenge is to provide a modular framework integrating existing tools and platforms like NVIDIA's DRIVE platform or NVIDIA's DRIVE SIM to increase the efficiency during development and homologation.

Digital Homologation with Simulation for R&D and validation using Nvidia GPU in cloud and HIL. Securing and homologating automated driving functions presents a huge challenge for market introduction due to an enormous number of scenarios and environment parameter combinations. Confronting conventional real world tests with the new challenges of automated driving is not feasible anymore, and yields to a virtualisation of the testing methods by means of X-in-the-Loop approaches. Especially when using Deep Learning Algorithms for automated driving functions, a scalable, powerful and consistent toolchain is required. Together with TÃV SÃD and the University of Applied Sciences in Kempten, AVL is working on such a consistent toolchain. Different configurations for the development and also approaches for the homologation shall be introduced. Especially the challenge of close-loop-testing, including the vehicle and powertrain dynamics, should be addressed. The challenge is to provide a modular framework integrating existing tools and platforms like NVIDIA's DRIVE platform or NVIDIA's DRIVE SIM to increase the efficiency during development and homologation.

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Topics:
Autonomous Vehicles, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Europe
Year:
2018
Session ID:
E8438
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