An effort to bring intelligent predictive navigation into the car is being mounted by a Pittsburgh-based start-up company named NavPrescience.
NavPrescience is a Quality of Life Technology Foundry spin-off based on 3 years of research at Carnegie Mellon University.
It will soon offer a nav system-enhancement package that can autonomously gather knowledge of a driver’s route preferences over time and suggest appropriate alternatives.
The new capability is intended to enhance a driver’s ability to safely and efficiently reach a destination on time with minimal stress. The patent-pending NavPrescience system should be especially useful in “keeping older drivers in their comfort zone” by matching individual driving capabilities and profiles, such as avoiding interstates and unguarded left-hand turns.
One of its underlying technologies, NavPrentice, is a routing system that leverages all available data that could influence path selection including time of day as well as real-time roadway information, traffic, and weather reports. NavPrentice then observes and, in effect, learns a motorist’s intentions, habits, and preferences to generate optimal routing solutions by emulating an individual’s patterns. A related subsystem, NavProphet, then accurately predicts the driver’s destination as well as favored routes and turns.
NavPrescience recently incorporated and has attracted some seed money funding from a Pittsburgh-area incubator called AlphaLab, which was established by Innovation Works, a venture capital group. Dey reported that the start-up firm is talking to potential commercial partners including GPS unit makers and resellers, car companies, and telematics services about incorporating the novel capability into their existing systems. The company hopes to ship its first product in the first quarter of 2010.
Note: This system is similar to BMW’s Ilena concept (Intelligent Learning Navigation). In ongoing evaluations on a BMW 3 Series, Ilena has shown itself to be accurate about 80% of the time, a capability that BMW researchers claimed will only improve with time. The machine-learning system is also able to analyze its fuel stores and then optimize the vehicle’s performance for a particular destination, potentially offering a 5-10% boost in fuel economy, they estimated. A market-ready product based on Ilena could appear in three to five years.