How High-Performance Computing in the Cloud Is Accelerating Advanced Driver Assistance Systems Simulations

How High-Performance Computing in the Cloud Is Accelerating Advanced Driver Assistance Systems Simulations

This presentation was made at NAFEMS Americas Seminar "Engineering Analysis & Simulation in the Automotive Industry: Creating the Next Generation Vehicle Accurate Modelling for Tomorrow's Technologies".

The automotive engineering community is now confronting the largest technology transformation since its inception. This includes the electrification of powertrains for more efficient consumption and cleaner emissions, the reinvention of the battery with fast wireless charging capabilities and finally the advent of a fully autonomous vehicle. Compounding to these technology changes, the automotive companies design verification process is moving away from a major reliance on physical testing to almost a full virtual simulation product verification process. The challenges to the automotive engineers are enormous and require a significant increase in the upfront use of numerical simulation capabilities, methods and processes such they’re able to efficiently design, manufacture and deliver these very innovative technologies to the market in greater speeds than ever before.

Resource Abstract

Advanced driver assistance systems, or ADAS, are designed to automate, adapt and enhance in-vehicle systems for road safety and improved driving experience. Multiple technologies are used to determine the vehicle surrounding conditions and to notify, alert, warn or control the vehicle if needed in order to support avoiding an accident or to provide a comfortable ride. These technologies require substantial simulation diversity and computational resources to achieve satisfactory product quality.

ADAS research areas can be grouped into five key technologies: (i) visual sensing via multi-cameras and computer recognition; (ii) sensing via LiDAR (Light Detection and Ranging), commonly used to make high resolution maps, and RADAR (Radio Detection and Ranging), used to detect other vehicles and objects in the vicinity but with a longer range; (iii) connectivity framework, providing geographic and traffic information, as well as peer-to-peer communication; (iv) artificial intelligence (AI) and machine learning (ML) algorithms to provide vehicle autonomy; and (v) automotive human machine interface (HMI) design.

Automotive manufacturers and tier-1 suppliers develop and validate ADAS in three ways: (i) testing the system on a prototype vehicle directly on the road; (ii) on a test-bench combining computer simulations of virtual vehicles or real-time embedded control systems with physical components, a technique more commonly known as hardware-in-the-loop simulation (HILs); or by (iii) using CAE simulation tools to model the physics of the complete vehicle or vehicle system under a wide range of different test scenarios.

Historically, most of the CAE simulation tools used for ADAS are run using a low number of core processors, typically 1 to 8 CPUs; either due to models that don’t require complex numerical methods for the engineering problems or mathematical physics that needs to be solved, or due to having model setups that rely on having fast communication speeds between the CPU and an embedded system or physical hardware connected to it. This limitation has forced some manufacturers to evaluate their models in only a handful of different scenarios from the broad spectrum of driving conditions their system might face on the field or to delay their development cycle on a market that is booming. Others, have started to look into leveraging high-performance computing (HPC) clusters to be able to run many more simulation conditions and in a shorter time span.

For manufacturers that already have an HPC cluster on-premises it becomes a question of capacity and managing priority queues with other finite element simulations that typically use this resource; for those who don’t have one, it become a question of cost, having to make a significant capital investment on a fast depreciating asset without knowing what the actual demand will be on an emerging technology as ADAS. Cloud HPC technology can be used for these applications providing unlimited computational resources completely on-demand.

This presentation focuses on leveraging an advanced engineering model simulation of an ADAS workflow across multiple solvers with the HPC large scale parallelization capability on the cloud. We will discuss the challenges to overcome when deploying an ADAS workflow onto the cloud infrastructure, including user interface, networking, and data storage. We will also demonstrate the benefit of deploying ADAS workflow on the cloud through case studies and a live example.