A key factor adding to the development of vehicles towards total automation is the colossal amount of data generated by a fleet of car sensors and their ability to connect to the cloud. Autonomous vehicles typically collect up to 4 TB of data every hour, which holds great potential for automotive ecosystem partners to learn about the driver, driving environment and accidents well in advance. Large amounts of data, anywhere from location and trip summary to engine health status and ADAS information, is stored for analysis and reasoning.
In Comes AI and Machine Learning
What works as a distinguishing factor in favor of connected vehicles is irrespective of being autonomous or not, the collection and uploading of this data on the cloud doesn’t stop. Access to data further enables all the entities involved in the transportation transactions to make intelligent, data-driven decisions and make way for new use cases.
A new challenge then arises to decipher the vast amount of raw data in a way that it could be used in a sensible manner to fuel a range of intelligent mobility services and networks. Machine learning mechanism proves beneficial in extracting the value of connected vehicle data, through a range of brilliant algorithms that unlock several applications and uses. What’s more, as the amount and complexity of this data increases, the ML algorithms get trained better to understand the dynamic mobility trends and decode the next set of automotive services.
Driving the Connected Vehicle Value Chain
Machine learning algorithms and AI tech interpret situations, environments as well as the right decisions to take, by consuming on connected car data. Up until an inventive approach to analyze the raw data in undertaken, the data simply remains a huge pile of un-converted resource. It is only after deploying AI and ML technologies that the actual use of connected vehicles comes into realization. As a result, ML algorithms enable the generation of insights that help navigate city roads in real-time, find shortest routes, interpret road signs, manage traffic flow and lane closures, and much more, giving the car a clear view of its environment.
- ML can be used to develop rich mobility intelligence, which in turn can optimize public transport links and micro mobility services in line with effective hyper local networks.
- The tech blend becomes critical in setting up EV charging stations, based on usage demand, as well as the types of stations required.
- Applications also include urban traffic intelligence in coordinating traffic signals to manage flow, prevent congestion due to road accidents in real-time as well as minimizing traffic violations.
- AI has been successful in tracking connected vehicles and their usage patterns, and conveying that information to first responders, nearby stores and service providers.
Strengthen your Product Capabilities with CEREBRUMX
Embark on the journey to vehicle connectivity with CEREBRUMX. Leverage the power of connected vehicle data combined with artificial intelligence and machine learning capabilities and safeguard future business prospects. With our AI-powered Augmented Deep Learning Platform (ADLP), we’ll help you make your auto business digitally ready and strengthen your product capabilities. We employ ML to extract valuable insights from the vast and complex data landscape with simple APIs and easy to integrate cloud infrastructure. Simply avail the flexibility of our data platform to enhance customer experience and create future-ready revenue streams.
CEREBRUMX believes in total consumer privacy and transparency when it comes to analyzing sensitive data. To ensure complete trust and uninterrupted business operations, we enable you to give the power back to the customers by letting them grant, revoke or edit consents easily, with our white label platform, CerebrumX Secure Consent.