Active Learning, Data Selection, Data Auto-Labeling, and Simulation in Autonomous Driving — Part 7
The last company, Aurora!
Aurora
Aurora, on the other hand, takes a slightly different approach. Rather than blindly pushing for increased mileage, they’ve maintained a focus on collecting high-quality real-world data and extracting the maximum value from each data point. For instance, they amplify the impact of real-world experience by identifying interesting or novel events and incorporating them into their Virtual Testing Suite, where they are used to continuously improve the Aurora driver.
However, not all real-world events are amenable to simulation in virtual environments. For instance, the exhaust of a vehicle may be of interest to an object detection system. Thus, using real-world data from such scenes to train the perception system to recognize and ignore exhaust can result in a more enjoyable driving experience.
The on-road events that they turn into virtual tests come from two sources:
- Copilot annotations: Vehicle operator copilots, who provide support from the passenger’s seat, routinely flag…
