On-road events become more impactful when we add them to our Virtual Testing Suite and use them to continually improve the Aurora Driver.
At Aurora, we’ve adopted a “smarter, not farther” approach to on-road testing. That is, instead of blindly pushing to drive more and more miles, we’ve continued to focus on collecting quality real-world data and on getting the most value out of every data point. For example, we amplify the impact of real-world experience by flagging interesting or novel events and incorporating them into our Virtual Testing Suite.
While they aren’t valuable as a measure of progress, on-road events can be incredibly valuable as learning opportunities. Our triage team reviews flagged events and then works with our engineering teams to identify which ones offer opportunities to improve the Aurora Driver.
One real-life situation can inspire tens or even hundreds of permutations in our Virtual Testing Suite, all of which can be continually used to fine-tune existing capabilities. In this way, one on-road experience becomes a multifaceted feedback loop for future versions of the Aurora Driver.
Read on to learn about Aurora’s Online to Offline pipeline, our process for rapidly converting on-road events into virtual tests.
Why we need on-road testing
We drive the vast majority of our miles in our Virtual Testing Suite, enabling us to make rapid progress at scale. But while we drive far more miles virtually, high-quality real-world experience is still important for developing the Aurora Driver.
First, real-world tests allow us to assess whether successes in virtual testing translate to the road. For example, will other drivers understand the Aurora Driver’s intentions when merging? We can simulate what other actors might do in various situations, but it’s important to observe how they actually interact with our vehicles.
Second, real-world data is useful for thoroughly training and testing our perception system. While we’re working at the cutting-edge of sensor simulation, it’s difficult to accurately simulate all artifacts of a particular sensor and the nuances of environmental conditions like dust, smog, and pollen. For now, real-world data remains important.
For example, the video below shows vehicle exhaust on a cold, rainy day in Pittsburgh. Early on, our perception system might have perceived the exhaust as an obstacle, causing the Aurora Driver to brake or nudge (adjust its trajectory) around it. We used real-world data from scenes like this to teach our perception system to identify and ignore exhaust, resulting in a better driving experience.