Anaeko Blog

How to Improve Autonomous Driving using Data Optimisation

04 Nov 2019 by Emma Foster 1 minute read

How to Improve Autonomous Driving  using Data Optimisation - Anaeko

Processing video and sensor data faster than your competitors is critical to becoming the world-leader in autonomous driving. Growing fleets of self-driving cars generating huge volumes of data means the cost and effort to manage these can get out of control. Data optimisation can help accelerate analytical research and data processing.

Autonomous driving teams need to optimise data processing, reduce infrastructure costs of storing and processing data, and operational costs of managing data, so that analytics teams can monitor more vehicles.

Enhanced analytics, deep learning and machine learning of raw data from autonomous driving will be key to improving performance of autonomous vehicles. With a focus on safety and reliability autonomous vehicle manufacturers need to effectively process huge amounts of data, cutting out the noise, to harness intelligent real-time insights to better understand an equip their vehicles.


A New System to Improve Data Processing

An automotive company generated hundreds of Petabytes of high def video files and high frequency streaming sensor data. Each test vehicle captured video files from 8 cameras and sensor data from 15 sensors. The test fleet transmitted 800 objects per second and captured over 5 billion video frames from a short trial. The company needed to efficiently search the camera and sensor data to identify common events and outlier incidents and to do this they needed to intelligently tag and curate all data.

After considering the technical and operational requirements a high performance system was created to process, analyse and improve operations, using:

  1. Parallel processing patterns
  2. A metadata search user interface
  3. Machine learning to enrich analytics
  4. Warm tier object store
  5. Deep-inspect algorithms

Download the full case study around Anaeko's iterative approach to understanding, analysing and integrating data optimisation solutions to improve performance for autonomous driving analysis.

Data optimisation for autonomous driving case study


Topics: data discovery, Data, Case Study, Data Optimisation, Self Driving Cars

Emma Foster
About the Author
Emma Foster

How can Anaeko help your company or government department?

Anaeko provides flexible analytics services tailored to organizational needs. For organizations starting with analytics we provide Analytics Accelerators, maximising current infrastructure and resulting in Proof of Value analytical reports. For established projects, we build scalable information architectures and optimise these for volume, variety and veracity at a truly National scale.

Anaeko provides integration services that deliver business value offering the following services:

Expertly-managed Remote Services

Accelerate Delivery

Agile Delivery Model

Executive Reporting

Delivery Management

Remote Agile and DevOps


Integrated Analytics


Analytics Accelerator

Data Readiness

Analytics Platforms

Self-Service Analytics

Predictive Analytics

Report Automation

Hybrid Cloud Discovery

Hybrid Cloud Plugins

Hybrid Cloud Transformation

Hybrid Cloud Migration

Hybrid Cloud Applications

Hybrid Cloud Integration


Data Optimisation


Data Discovery

Data Classification

Data Integration

Data Management

Data Analytics

Intelligent Processing

Application Design

Application Development

Test Automation

Continuous Integration

Continuous Delivery

DevOps Reporting

Multicloud DevOps



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