Automotive human-machine interface (HMI) systems are designed to enable drivers to interact with their vehicles ,customize, use the humungous amount of modes and features available for customer use. HMI can establish a more natural interaction between humans and machines through touchpads, buttons, or speech systems. The increasing demand for connectivity solutions, low-cost HMI software, and enhanced user experience (UX) has made HMI solutions like head-up displays (HUD), rear-seat entertainment systems, steering-based controls, instrument clusters, and voice command systems popular in the automotive market. But before implementing and deploying such softwares usually on android, they have to undergo a series of testing to ensure the product stability and maturity.
overall the (now) smart core connected clusters and info tainment system solutions help automakers provide a more natural and intuitive HMI system that ensures safety, comfort, and convenience for drivers. To reduce the time and effort involved in documenting one time issues and for easy and automated report generation this project was developed.
As in smart core both the cluster and infotainment system is connected as one.
But for the proper deployment of software in edge devices ,they must be properly tested. As the sofware will be very unstable.
For the rectification of screen issues and their fixing ---> Identification is the first step.
This app aims to make the testing semi Automated , by automatically segmenting video components that contain the screen issues.
So that they can be easily rectified.
I'm immensely grateful to Mahindra research valley for letting me work on this project.
$ wget https://raw.githubusercontent.com/jasondeglint/tf/main/install_tensorflow.sh
$ source install_tensorflow.sh