Case study

Drivable prototype vehicle with multi-domain learning vehicle architecture.

Industry: Automotive

Customer: North America car maker

Drivable prototype vehicle with multi-domain learning vehicle architecture.

Industry: Automotive

Customer: North America car maker

Competence domains:

  • Product ideation & strategy
  • Data analysis & AI solutions
  • Machine learning & Big Data processing

Challenge

A drivable prototype vehicle called for the creation of an “operating system for adaptation” that enables multi-domain learning and predictions. The resulting product will demonstrate how a multi-domain, learning vehicle can be architected and deploy a technology that can deal with multiple ML engines, developed by different suppliers, into a real car.

CloudMade provides a solution (ETL pipeline and data lake architecture), which allows data science and analytics teams to work on the data simultaneously, without spending a lot of resources on the infrastructure and receive insights in
minutes.

Solution

CloudMade has provided full end-to-end solution architecture, design and development of the core platform components including in-vehicle and cloud portions of the system. The solution utilized pre-existing facilities of the OEMs next generation vehicle platform as much as possible, with the HMI and intelligent features development made by the OEM’s in-house development teams.

Additionally, CloudMade provided consultancy and worked in co-creation mode with the OEM in defining intelligent use cases and integration into the OEM’s connected vehicle infrastructure.
The resulting system has been launched in a drivable vehicle and currently used as a research platform for exploring and developing new intelligent use cases and adaptive features.

Areas of responsibility

  • System architecture and design
  • Leading ML and Data Science part
  • Learning platform development and adaptation
  • Consultancy on design and development of 5 intelligent use cases including value proposition, UI and UX.

Technology

  • Cloud learning system and services
    Microsoft Azure; Azure blob data storage; AMQP (RabbitMQ); REST APIs written in Java with Spring framework; Spark; SparkML; Rundeck for pipeline orchestration; Hadoop (Hortonworks 3.1); CloudMade’s SDK and learning jobs (C++, Java, Python); Web-tools for researchers (Angular 2).
  • Edge computing and embedded components
    QNX 7.0; proprietary OEM’s message transport facilities; QT-based proprietary OEM’s framework for HMI development; CloudMade’s SDK (C++); CAN and multiple proprietary interfaces to access telematics and sensor data in the car.

Tell us about your challenge. At CloudMade we are ready to provide expertise and support.

Contact us