Illustration: Rebecca Zisser/Axios
Simulation is a crucial tool in the development of self-driving cars, but it has constraints, especially regarding the speed at which it takes in data and makes decisions.
Why it matters: In the absence of significant amounts of data from autonomous vehicle road testing, companies like Waymo, Uber and Tesla are investing in self-driving simulations, with billions of miles in virtual simulation tested to-date.
How it works: Speed plays a critical role in simulations, because if sensing does not keep up with the rate at which events actually happen, it limits the vehicle’s ability to respond effectively to critical real-time, rate-dependent phenomena and adapt its driving pattern accordingly.
Additionally, if a simulation does not process an event quickly enough, it could lose track of how the consequences of key events affect future behavior, leading to a loss of fidelity in both data and control of the vehicle.
Between the lines: In a research setting, advanced cameras in vehicles with automated features typically capture images at a speed of up to 300 frames-per-second, and this speed informs simulations.
But cameras in anticipated road models are more likely work at just 30 frames-per-second and this 30 millisecond lag-time is significant, as it limits the ability of a vehicle to identify and respond to an event in near real-time.
Meanwhile, dense urban environments filled with pedestrians, unusual traffic patterns, and construction increase the amount of computation that needs to occur nearly instantaneously.
Additionally, as the technology in vehicles catches up to and surpasses what it is used in simulations, current simulation data might be rendered obsolete.
The bottom line: Data from varying sources, simulations included, will be crucial to getting AVs safely on the roadways — but simulation technology can improve when it comes to speed, and those improvements will yield better data and stronger AV training.