Automotive Companies only Access 5% of their Vehicle Data
- Author Alexandra Nguyen
- Published April 16, 2021
- Word count 725
Although vehicle sensors collect massive amounts of data, only 5% of it is currently being used for product development. Better infrastructure and data processing hold the keys to progress.
The promise of fully autonomous vehicles continues to excite and inspire millions of people around the world. The amazing things that safe, reliable, self-driving vehicles can do for humanity--from providing newfound mobility to senior citizens to reducing traffic accidents--are closer to our reach than ever. But we still have a long way to go.
Along with the fuel (gasoline, diesel or electricity) that powers automobiles, autonomous vehicles require a fuel of their own to “drive” safely and effectively: data. Although that data, already collected by millions of sensors on thousands of vehicles around the world, is readily available, it’s not being utilized to its full potential.
Sensor datasets power the algorithms that make all levels of autonomous driving possible. As automotive companies aggressively pursue data-driven product development, data handling (including the exploration, querying, curation and evaluation of data) is a common bottleneck on the road to progress.
These essential but hard-to-manage datasets bring a unique set of challenges for those hoping to make use of them:
Unstructured and diverse formats
Need for rich semantics in order to access them
Huge sizes that require high-performance computing
Strong need for data versioning
Access and security issues
Need for continuous failure-case driven data exploration
With the proper data processing and analytics software, engineering users can overcome all of these challenges and vehicle data can fulfill its potential. By upgrading legacy technology for data access and analysis, OEMs and mobility tech companies can bump up data utilisation rates by up to 40% and generate additional ROI as data exploration, search, analysis, anomaly detection, and evaluation require less manual engineering work and yield better results.
Data infrastructure and management are being neglected
Currently, vehicle technology developers are focused on machine learning models and ground truth labeling. These same developers are neglecting infrastructure upgrades, leading many to use legacy technology for data management. Terabytes of unstructured, unprocessed vehicle data easily overwhelm these systems, causing them to malfunction. Raw data has no metadata and billions of frames, and technology developers are left to use tools ill-suited to the task of data management to organize data on their own.
Highly-paid engineers search datasets manually on sluggish database systems, spending up to 75% of their time on raw data handling issues instead of training and validating models. Furthermore, the lack of insights garnered from these vehicle datasets means machine learning and data science systems are unable to effectively build AI functions that rely on sensor data as an input.
Purpose-built data infrastructure is the solution
Infrastructure that is designed and built specifically to house raw sensor data and extract insights is the answer. This infrastructure should be as simple and easy to navigate as spreadsheets and SQL databases that bring order and usefulness to data in other industries. For vehicle sensor data, infrastructure that uses a flexible and minimalist data model and a scalable method to produce semantics, along with fast queries and integrated endpoints to use and share data, is most effective.
These and other infrastructure elements improve data re-use and result in faster insights. Enabled by semantic automation, data can be ranked according to its importance based on context, content, criticality, and usability. This reduces redundancy and maximizes information density. Low-importance data is archived or deleted, while high-importance data is easily accessible for everyone. Furthermore, data insights can be provided almost immediately with overviews of the content and the redundancy.
Users can augment retention recommendations with tailored rule-based constraints, e.g. unprotected left turns should always be kept.
Better autonomous vehicles, sooner
Advances in automated data infrastructure and processing liberate automotive technology developers from the constraints of legacy technology systems. Automated, semantic data management systems such as SiaSearch make data handling easier than ever, enabling automotive companies to save valuable engineering time, boosting productivity and increasing overall utilization.
Put simply, more robust, effective infrastructure for unstructured sensor data will lead to higher ROI on research and development by freeing up engineers to do what they do best: building algorithms that will power the autonomous vehicles of the future. And with those engineers working more efficiently, the dreams of autonomous vehicles improving our lives are that much closer to reality.
Dealing with lots of raw sensor data? Learn more about the SiaSearch data management platform: https://www.siasearch.io/
Originally published by Clemens Viernickel on: https://www.siasearch.io/blog/raw-sensor-data-needs-infrastructure-to-be-useful/Article source: http://articlebiz.com
There are no posted comments.
- Why your business needs cloud services?
- Industry 4.0 and Smart Factories
- What is Configuration Software - HMI MMI SCADA
- Basics of Industrial Control System and the Use of PLC
- Different Types of Pressure Transmitters, Working Principle, and How to Select Pressure Transmitter
- Rise of Digital Car Control Console and Application of Embedded HMI System
- How to Install Hollow Shaft Encoders, Problems & Solutions
- Programmable devices for IoT
- How to Install Hollow Shaft Encoders, Problems & Solutions
- Do You Really Understand Human Machine Interface (HMI) - HMI Q&A
- How to Install Encoders - Best Practices
- VFD Basics - How VFD Controls the Motor Running?
- Study on the Performance of Single Phase AC-DC-AC Inverter Circuit
- Advantages of VFD for HVAC System
- Piston Compressor Basics — Structure, Working Principle, Advantages and Disadvantages
- What Are the Factors to Consider When Selecting Rotary Encoders?
- Air Compressor Troubleshooting and Solutions
- Develop and maintain multiple database engines with one platform
- Programmable MQTT Devices - NORVI
- How to Use a Rotary Encoder?
- Factors to Consider When Selecting Rotary Encoders
- Comparison of central air conditioning- Toshiba vs Mitsubishi vs Daikin
- Maintenance of Absolute Encoders
- How to Earn Free Robux in Roblox
- It’s Time to Fast-Track Your Managed IT Services Like The Pros in 2021
- What Are the Major Causes of Variable Frequency Drive (VFD) Damage?
- POST PRODUCTION SERVICES
- Beta Testing – An Informative Guide
- Our Windows App Got Better with New App-Based Troubleshooting Features