Neuromatic Auto is capable to Monitor Driving Effectiveness in real time.
Neuromatic Auto is a solution to improve existing Driver Assists Solutions because of Brain Activity Tracking, with EEG Sensors, for the most effective drowsiness Detection (brain based warning before it is to late).
Our unique, patent pending, scoring model and ecosystem converts the drivers performance data into a points based evaluation system for the Usage-Based Insurance type (UBI) unique, because it monitors continuously while driving.
This accurate data information, based on biosensors, telematics and an ecosystem with environmental data is then evaluated and achieved.
Neuromatic Auto ecosystem includes:
1. Wearable headset or Smart glasses with biosensors connected to a smartphone and it Detects when the Driver gets Drowsy (DDD) to predict stage 1 sleep warning to prevent sleep-related accidents, as well others reasons like, aggressive driving style, uncontrolled driver condition from alcohol, narcotics soporific or unhealthiness.
2. The Mobile application supports Apple CarPlay, Android Auto, connects to onboard OBD2, Head-Up Display (HUD), shows brain attention levels, sends alerts, push notifications and saves biosensors and telematics data to the cloud storage.
3. The Web application handles personal data from the cloud storage and calculates the Driving Effectiveness in 6 levels based on brain measured activity, time of day (night, morning, day), location (urban, interurban, mountains ), distance, weather, speed violations, age etc. and displays stats, trends, evaluation archives (KPI) and group sharing.
The auto insurance industry’s Usage-Based Insurance (UBI) approach, according to new research the on-board technology to report drivers’ real time driving behavior, will benefits both: the motorist and the UBI insurer,
Trucking Fleets, Personal Usage and commercial drivers (Lyft, Uber, Self driving cars testing and teaching innovators can use this tools in order to monitor the Drivers fitness and condition on the job!
The traditional model for setting auto insurance premiums has been to base rates on the motorist’s driving history, age, gender and even marital status (in some states). Thanks to new technological options, insurance companies and motorists have started to work together to give the insurance companies access to better data on an individual driver’s risk level, and give the same driver a sense of greater control over how much he or she will pay in insurance premiums. Over time BIG data can further reduce accidents and achieve a fair auto insurance market.
Usage-based insurance (UBI) is a recent auto insurance innovation that enables insurers to collect individual-level driving data, provide feedback on driving performance, and offer individually targeted price discounts based on each consumer’s driving behavior. Using individual driving behavior (from sensor data) and other information for UBI adopters, we estimate the relationship from being enrolled and monitored (for up to 26 weeks) in the UBI program and changes in the driving behavior of UBI customers. The key results of our analysis show that after UBI adoption, the UBI users improve the safety of their driving, providing a meaningful benefit for the individual driver, the insurer, and society as a whole. While UBI customers decrease their daily average hard-brake frequency by an average of 21% after six months, their mileage driven does not decrease comparing week 26 to week 1. We also find heterogeneous effects across different demographic groups. For example, younger drivers improve their UBI scores more than older drivers after UBI adoption, and females show more improvement than males. Furthermore, we find evidence that negative feedback and economic incentives correlate with greater improvement in driving behavior. Our results suggest that by sharing private consumer information with the insurer, UBI can benefit consumers who become better drivers, as well as the entire society from improved road safety.
(Authors:Miremad Soleymanian , Charles B. Weinberg , Ting Zhu, Publication:Marketing Science)