Mon, 29 Oct 2018
In a world where AI is taking more control in human decision making, systems supported by digital twins can prove game-changing for the automotive industry. Digital Twins are key to the long-term success of autonomous vehicles in the future.
Digital Twins are used in the automobile sector for creating the virtual model of a connected vehicle. They can capture the behavioural and operational data of the vehicle and analyse the overall vehicle performance, delivering a personalised service for customers.
The digital twin is either viewed as an imitation of a model or as a sum of connected components. Engineers delve into AI to design the ideal automotive product way before it enters the production line. Simulations can be analysed to see why and how future problems such as breakdowns occur. Using digital twins for autonomous vehicles will potentially save many unplanned costs and miles through road testing and maintenance.
Collaborating with operational teams and analysts is ideal for gathering data to start modeling processes. It enables engineers to observe how a vehicle will perform in any condition through gathering data on types of motor, suspension, structure, aerodynamic body they want to tap into, and the materials they are built from.
Automotive manufacturers can create a digital twin of every single autonomous vehicle it sells, enabling them to analyse how a car performs in its physical environment, and track the vehicle from its creation to the day it goes to the junkyard. Much of the sensor infrastructure is already in place in newly released vehicles with them containing up to 100 sensors monitoring critical systems. This data can be combined with design information so predictive analysis can be implemented. An example of where this could be useful is for the modeling and monitoring of airbag systems. Defects would be spotted far sooner, and many potential injuries would be avoided.
When a mechanic runs diagnostics at an auto shop, data from using a digital twin can show what specifically needs to be repaired. The next step would be setting up a system for the data to automatically be transmitted back to the manufacturer. The manufacturer can analyse the data and perform predictive analysis to help make driving experiences smarter and safer for car owners. Engineers are also able to predict performances of products within large systems such as; an individual wing on a plane, a rocket engine launching, an office building maintaining energy through the day, A race car engine which is about to burn, or even a driverless car navigating the road during rush hour.
While being highly intelligent, digital twins are not fully autonomous and will require human intervention. The manual testing of new features and modifications of physical assets through virtual replicas are a particular example. AI, while not necessarily providing more intelligence than humans, is certain to boost skills through implementing more efficient and productive analysis. While the automotive industry has the relevant processes to move towards further implementing digital twins, they will still need the ability to include individually derived data.