All of us now expect the same level of mobile experience in the car, on a train, or moving around the city, as we get at home or in the office. Streaming video and audio, conference calls, and virtual assistants—all of these demand a reliable connection. This is why connected vehicles, and the whole connected experience across the highway network, are becoming increasingly important for modern life.
The emphasis is now on vehicle connection. Subscribers want a seamless, near-perfect connected mobility experience. As connected and autonomous vehicles become more common, the challenges will only multiply.
With the progress towards connected and autonomous vehicles (CAV), network under-performance is simply not an option. The self-driving car looks set to be a bandwidth-hungry mobile device in its own right, with a continuous need to offload many terabytes of vehicle data generated by sensors, cameras, and on-board data processing. With a huge software footprint, and with shared transportation models reducing down-time, even over-the-air software updates will be a major network challenge. Optimizing the connected mobility experience is becoming a top priority.
Conventional methods for assessing mobile coverage and quality don’t reflect how our usage of mobile connectivity is evolving. Consumer expectations for connectivity demand a superior, always-on network experience, mirroring the excellence in all-round user experience already promoted by vehicle manufacturers. There is a pressing need to put real mobility at the heart of our mobile experience analytics, and to see metrics from the point of view of the highly mobile user.
Connected Cars also need a high-quality, uninterrupted service for optimum monitoring, and in emergency or safety-critical situations where fractions of a second can make all the difference. Additionally, always-on connectivity is valuable for enabling aftermarket opportunities, using contextual data for advertising and marketing promotions.
The availability of new connected mobility analytics based on Artificial Intelligence (AI) makes it possible for operators to improve connected mobility by accurately pinpointing routes with problems. Drawing call data anonymously from the network, voice and data usage patterns can be analyzed and processed with finely-tuned Machine Learning (ML) and AI algorithms to generate a heatmap of connection quality across all major travel routes. This allows dynamic network optimization that keeps the connected vehicle experience for passengers and drivers as close to perfection as possible.
The AI-based pattern recognition technology also provides visibility into drivers’ and passengers’ experience patterns along their travel routes, providing useful information to businesses serving the CAV markets as well as helping car manufacturers to get closer to the customers who travel in their cars. Vehicles encountering service issues can be identified and issues resolved, either on the move or by recalling the vehicle for remedial action.
The vision of the autonomous car is critically dependent on the existence of continuous, high-quality connectivity, and connected mobility analytics is the best way to ensure this is available.
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