Predictive Road Conditions

Look-ahead algorithms anticipate the road condition before you drive it, giving the vehicle the opportunity to adapt or alert the passenger to improve control and mobility. These predictive algorithms are based on years of studying the interaction between the environment and surrounding features such as buildings, trees, mountains, water, overpasses, bridges, and traffic, as well as behavior of drivers, accident likelihood and severity, and fuel efficiency of all vehicles. By collecting our own proprietary data using the Scout, we have developed custom predictions that are meaningful to autonomous cars, electric vehicles, and fleets. Machine learning algorithms trained our predicted conditions to the road surface measurements from the Scout to produce this one-of-a-kind dataset.

Our predictive road condition data is critical for optimizing the fuel efficiency of cars, extending the distance traveled for electric vehicles, and improving the control of autonomous vehicles in unsafe conditions. Using our predictive data, Level 3 and 4 autonomous vehicles can give passengers advanced notice of when to take control of their vehicle. This data also serves as the backbone for our adaptive cruise control, pathfinding optimization, precision GPS, and cooperative control algorithms.

Vehicles and individuals can access this data by connecting to our infrastructure via our API, mobile application, and web application.