The evolution of both fleet and consumer telematics technologies over the past decade has allowed for valuable content to be pushed into the connected car. While industries such as traffic, navigation and entertainment have taken advantage of this new “in-vehicle” marketplace, the large players in the weather industry have lagged behind.
At the moment, weather content is generic and doesn’t provide actionable information to the driver during high impact weather events. IoT solutions, like Fathym’s surface weather forecast, can provide more useful information to support a driver’s reaction to changing weather conditions. Information about slick roads, low visibility, high crosswinds, lightning, floods and tornadoes is available now and will be integrated into the connected car in the future, providing programmatically-generated micro-forecasts of the weather conditions immediately in front of it. These micro-forecasts and tire friction data can then be utilized to send predictive pavement condition information back to the vehicle’s Engine Control Unit (ECU) to dynamically enhance the car’s Electronic Stability System (ESS), and further optimize suspension, hard braking and torque management in adverse weather conditions.
From Human Drivers to Computers
Actionable road weather information is here to provide data to the drivers of connected cars, but autonomous vehicles are on the horizon and this data will be even more critical as the transition occurs from the human driver to the computer on the autonomous vehicle. With approximately 25 percent of car crashes occurring during adverse weather conditions, tactical weather information will be critical to the autonomous vehicle network in order to provide a safe driving experience. Very high-resolution road weather observations and forecasts will also be critical for the autonomous vehicle to dynamically adjust the way it handles changing road and weather conditions and the route it takes.
Enhancing the Vehicles of Today and Tomorrow
Fathym’s surface weather forecast offers a statistically-based, machine learning road weather forecast that is dynamically-generated, globally-scaled, and enables sub-kilometer forecasts, alerts and weather risk variables, such as delay and slick roads. Route and geographic point forecasts are available via API.
Fathym’s hyper-local forecasts provide the ability for connected and autonomous vehicles to make better road weather-related decisions, as well as the forensics to understand accidents caused by weather. This enables connected and autonomous vehicles to adjust their behaviour to ensure the safe delivery of occupants to their destination.