New Perception Metric Balances Reaction Time, Accuracy

Both elements are critical for applications such as self-driving cars. Researchers at Carnegie Mellon University have developed a new metric for evaluating how well self-driving cars respond to changing road conditions and traffic, making it possible for the first time to compare perception systems for both accuracy and reaction time. Mengtian Li, a Ph.D. candidate in CMU's Robotics Institute , said academic researchers tend to develop sophisticated algorithms that can accurately identify hazards, but may demand a lot of computation time. Industry engineers, by contrast, tend to prefer simple, less accurate algorithms that are fast and require less computation, so the vehicle can respond to hazards more quickly. This tradeoff is a problem not only for self-driving cars, but also for any system that requires real-time perception of a dynamic world, such as autonomous drones and augmented reality systems. Yet until now, there's been no systematic measure that balances accuracy and latency - the delay between when an event occurs and when the perception system recognizes that event.