Mobile and Embedded Prediction
Solutions for the Intelligent Vehicle
Enhancing the driver and vehicle profile at the heart of navigation, cockpit, seating, chassis, powertrain and more
Benefits
Increase your speed to market and decrease your cost and risk in the deployment of machine-learning.
Launch your own in-house features or those developed by third parties.
Buy custom CloudMade use cases off the shelf.
Using CloudMade solutions you can:
- Transform the user experience
- Enable smarter, safer journeys
- Form the basis of new revenues (OEM and partners)
CloudMade’s framework allows you to maximize data value using 3 learning approaches.
A unique architecture
for learning.
Our flexible frameworks architecture allows you to deploy CloudMade components within your own solution, leveraging your vehicle or smartphone sensor network and then computing onboard and/or in cloud to deliver predictions back to the device.
Use cases for the Intelligent Vehicle
The driver profile at the heart of navigation, cockpit, seating, chassis, powertrain and more.
The future of automotive lies in connectivity and smart use of driver data to improve the user experience. CloudMade has been developing use case solutions to address this future for over 10 years.
CloudMade has been developing use case solutions to address this future for over 10 years.
Some key use cases that our Intelligent Vehicle solution enables include:
- Streamlined interfaces that predict the driver’s next destination, maneuver or route
- Interfaces that learn the driver’s preferences and habits
- Proactive assistance in the vehicle for cabin and climate settings
- Personalised, contextually relevant search results
- Safer driving with integration into ADAS
- Adaptive displays that manage information flow to drivers ensuring they stay focused on the road
CloudMade is ready to work with you to pick the right use cases and technical package that enables your brand to create a truly intelligent, connected vehicle.
Our current Intelligent Vehicle package contains all of the above use cases in production, pre-production or ready-for-testing status. Please get in touch for more information about our Intelligent Vehicle solutions.
Use Cases for Companion and Mobility Apps
Smartphone apps – as complete product or SDK modules to enrich your current solutions.
Intelligent Apps deliver great digital user experiences to drivers and passengers by integrating high levels
of personalisation, modern technologies, and state of the art design.
Data from mobile usage, blended
with that learnt from vehicle
use, creates a total experience for the user wishing to make a journey or find out more information about
previous journeys.
Some key use cases that our Intelligent Vehicle solution enables include:
- Streamlined interfaces that predict the driver’s next destination, maneuver or route
- Simplified communications through calendar and contacts integration to journeys
- Trip history and categorisation, both from mobile only-data and vehicle data
- A holistic information experience with driver profile, weather and traffic integration
- GDPR Compliant data collection and privacy controls
CloudMade is ready to work with you to embed the relevant use cases and technical package that enables your brand to create a truly intelligent Companion app for your brand, or integrate new intelligent features to your in-vehicle web-app platform of choice.
Please get in touch for more information about our Intelligent Apps solution.
Adaptive Framework
The industry-leading cloud & SDK product for collecting and analysing automotive data sets.
The key will be combination of proactivity, personality and ability to manage tasks, based upon learning about your personal data.
Adaptive Framework components
CloudMade’s Adaptive Framework allows you to build intelligent
mobility solutions faster and with more flexibility.
Select the jobs you are focusing on to find out more about the different framework
components and how they can work for you.
Feature preparation components
Toolchains that check the quality and integrity of feature data to create reliable datasets.
Journey builder
Processes data feeds across devices. Provides enrichment from third party sources and builds journeys for IE use.
Data validation
A process to filter and report on the quality and integrity of data feeds.
Data validation reporting
Reports and alerts on data validation.
ML delivery & distribution components
Manage the creation, updates and distribution of personal profiles that enable predictions across cloud, phones and vehicles.
Inference engine scheduler
Uses standard scheduling software as a base to schedule IE jobs.
Model repository manager
A repository to store personal inferences.
Profile builder
A user-device Edge service for management and profile distribution.
Cloud predictions REST APIs
APIs that provide a wrapper for the execution of prediction plug-ins in the cloud for use in portals, web apps or via web-api from other devices.
Context Monitor
A process that monitors context signals to determine whether predictions need to be updated, and if so executes them. Predictions are published and available at any time for standard context. Predictions for specific context (including what-if ad hoc requests) are available on demand via RPC like mechanisms.
Profile synchronizer
An in-vehicle process that manages the local cache of user profiles from cloud or on-board learning. On-board and off-board machine learning for a user is merged with specific on-board or off-board algorithms.
Security components
User authentication and confirmation; GDPR compliancy; onboard and offboard resiliency.
SSO service
User authentication service.
Data injection components
Acceptance and pre-processing of data from the automotive eco-system; passing features to events.
Data Import for appliances
A tool to load data into an appliance event feeder.
Event feeder
A service that accepts and processes data from multiple sources including vehicles, vehcile telematic systems, data lakes and mobile devices.
Streaming event feeder
A pre-processing stage to accept streaming data and execute feature extraction with links back to the source stream (source / index to allow inspection), and passing features to the event feeder as events.
Munic.io data processing toolchain
A toolchain to process and collect data from munic.io dongles.
Event logger
A process that accepts events, manages them in a queue, implements storage managment rules and submits the stored events for synchronization with cloud systems according to business rules.
Visualization dashboards components
Web-based dashboards for production maintanance and data science research.
Driver dashboard
A dashboard for exploring profiles and data.
Validation framework dashboard
A dashboard for production maintance or data science research related to the validation framework.
Journey/profile/etc viewer
Not customer facing, but provides a basis for enhancements to the driver dashboard.
Inference engines components
Use-case centric machine-learning algorithms for intelligent mobility.
Python inference API plugins
A set of plugins that provides learning (IE) and predictions for a specific domain.
Predictive routes and destinations inference engine
A job for creating predictive routes and destination profiles for vehicle predictions.
Prediction plugins 1 per engine
A portable plug-in that provides predictions for specific IEs.
Java IEs in cloud 1 per inference
A job for creating and training a ML model for specific IEs.
Machine learning API components
Components that deliver APIs for external service consumption.
Python inference API
A component that provides python API for model development in python.
Data API for prototyping (integration with sagemaker, etc)
A set of cloud-specific views that allows for working directly with journey data from within cloud-provider ML tools. Not implemented yet, but planned.
On-board learning manager
A vehicle process that manages and executes on-board inference engines to locally create inferences for inclusion in device profile and execution.
ML utility libraries
A broad set of Machine Learning algorythims tuned for use in learning and prediction on vehicle and mobile device architectures.
Geo data management components
Toolchains that enable hybrid geospatial data management processing for caching, searching and tiling services.
Map data import toolchain
Processes that convert, import or update third-party map data into a hybrid format.
Fleet learning server
An integrated set of processes that are a base for crowdsource data from various sources that feeds into hybrid map datasets (e.g. ACC usage).
Hybrid server
Service for efficient layered geo-data distribution.
Hybrid onboard server
An on-device service that manages local cache and services clients across the device/vehicle.
Hybrid client
A library that provides access to local hybrid cache and optionally syncs with a hybrid server, as required.
Hybrid place naming
A service that matches geo coordinates to place names using a variety of approaches. This feature can potentially integrate with proprietary services.
Hybrid streaming data management
A pre-processing stage to accept streaming data and execute feature extraction with links back to the source stream (source / index to allow inspection), and passing features to fleet learning or other hybrid storage systems.
Validation methods components
Quality checks for driver profiles and machine-learning algorithm predictions.
Validation framework
A set of jobs for checking the quality of machine learning algorithms.
Profile quality measurement job
A job that evaluates a driver’s profile quality using validation framework results. Enables profile suppression. Might be a part of profile builder.
CloudMade’s framework products are the result of years of development and are available right now.
If you are thinking of developing your own machine-learning framework, talk to us first before commiting valuable budget resources for internal development. Please get in touch for more information about our any of our framework components.
Products and Services
Use the adaptive framework to power value-adding experiences.
Inference Engines:
- Specific machine-learning algorithms that turn data into predictions
- Run individual use-cases or combine inferences for complex, hard to copy features that enhance brand value
- Use your own or third party algorithms, or use CloudMade’s tried and tested product
Products and Solutions:
- Touch the end-user with amazing personalized services
- Use the Adaptive Framework to quickly spin-up new experiences
- Build machine-learning powered infotainment bundles that can be monetized
- Use your customers’ data for cost and warranty reduction across the entire fleet
Algorithms and Products/Solutions are all deployed on top of the Adaptive Framework
CloudMade’s Adaptive Framework allows you to design your intelligent vehicle architecture to maximise reusable components, then build and deploy services on top of that at your own pace.
We are constantly analysing and validating the quality of our algorithms and improving performance. If you are thinking of developing your own machine-learning inference engines or the services running on top of them, talk to us before using your own valuable in-house development resources.