Connected car data with advanced technology is making revolutionary changes in the automotive industry. With real-time data, the car owners can now have a more safe and enriched driving experience. While the existing cars come laced with plenty of safety features, analysing the connected car data can additionally provide valuable information to ensure further safety of the driver and the vehicle.
Here are the five ways in which connected car data is ensuring driver and vehicle safety –
Assisting in emergency situations
With connected car data, first responders can gain real-time information about a vehicle’s location, state of the road, traffic situation, nearest aid-centres or fuel station to assist the driver. This can prove to be helpful for faster action to ensure safety of the driver.
Planning better road infrastructure
Historical data on accidents and breakdowns due to poor road infrastructure can be used by the government for better planning and fixing problems like – potholes, bumps, uneven pavement, stalled vehicles, or road obstacles- thereby reducing the likelihood of accidents.
Pre-empting vehicle failure
With real-time notification, automated odometer reading, timely alerts and warnings for tire pressure, drivers can regularly track the maintenance and performance of their vehicles to ensure safety on road.
Encouraging safe driving
Connected car data can be used to provide immediate feedback and improvement tips to the driver for on-road safety. The real-time data can help a self-aware driver overcome the chances of human error by warning them about safety risks through vehicle sensors and alerts, thereby reducing the chances of collisions.
Reducing car thefts
With the help of connected car data, vehicle safety can be ensured by advanced features like secured digital lock systems, door lock and unlock alerts, ignition status, geo-fencing and location tracking, and sending vehicle activity notifications even when the vehicle owner is away from the car to prevent potential car thefts.
About Us –
CerebrumX, with its augmented deep learning platform (ADLP), addresses the critical problem of driver and vehicle safety. ADLP ingests real-time data from vehicles and augments it with contextual data to create unique datasets that allow different stakeholders to deliver segment-specific solutions. ADLP’s built-in AI engine provides various inference models to get otherwise complex insights on a real-time basis, thereby enabling ecosystem partners to ensure these safety measures for an enhanced end-consumer experience.