Why simulation is the key to building safe autonomous vehicles

October 04, 2019 // By Jamie Smith
I recently had the honor of moderating a lively panel discussion on testing autonomous vehicles (AVs). The panel showcased industry experts with a deep understanding of the challenges and the insights necessary to address them.

The moderator and panelists from left to right: Jamie Smith,
Celite Milbrandt, Thomas Herpel, Paul Boberly, and Matthijs Klomp.

While everyone agreed with the importance of building a safe Level 4 (L4) autonomous car, many of the existing AV developers are still looking for the “North Star” to guide their test methodology. We brought together a broad collection of perspective—senior leaders from an OEM, suppliers, and a simulation startup—to share their perspectives:

  • Matthijs Klomp, Vehicle Motion and Climate Solution Architect, Volvo

  • Thomas Herpel, Test System Development Manager, Zukunft Mobility (a company of ZF)

  • Paul Borbely, Tools Development Manager, Valeo

  • Celite Milbrandt, Founder and CEO, monoDrive

Here are the three major takeaways I got from the discussion:


Unique challenges

No industry organization has laid out a ratified testing methodology for L4+ autonomous vehicles. The consensus from the panelists is that though regulations are behind, testing is critical to build safe autonomous cars. Currently, automotive companies are very good at testing the individual components, whether it’s a camera, radar, or LiDAR. This has proven effective for building L1 or L2 autonomous vehicles.

Such systems have helped human drivers drive more safely. However, if we want true autonomous vehicles on the road, we need to figure out how to test complex systems with many advanced sensors sharing information with a processing system that uses artificial intelligence (AI) to identify all objects around the car to make the best and safest decision. The panelists reiterated the complexity of these systems. 

It’s important to understand that AI may interpret information differently based on very subtle changes in the image, radar sensor, or LiDAR sensor data cloud. When the sensors are integrated with a central computing component, the OEMs and suppliers will need to develop, test, and validate the brain with the AI algorithms to be able to correctly identify roadblocks, pedestrians, cars on the road, and surrounding environment, and in turn decide the safest path forward.

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