EsoftThings works with Renesas’ R-Car to realise autonomous driving

EsoftThings (EST) has announced it has joined Renesas’ R-Car Consortium to accelerate the development of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) with computer vision technology optimized for R-Car systems-on-chip (SoCs). The Renesas computer vision and cognitive Accelerator IMP-X5 of the R-Car SoCs are designed with the intention of processing huge amounts of real-time input from cameras and radar sensors that are being added to future models of cars.
November 8, 2017 Read time: 2 mins
EsoftThings (EST) has announced it has joined Renesas’ R-Car Consortium to accelerate the development of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) with computer vision technology optimized for R-Car systems-on-chip (SoCs).

The Renesas computer vision and cognitive Accelerator IMP-X5 of the R-Car SoCs are designed with the intention of processing huge amounts of real-time input from cameras and radar sensors that are being added to future models of cars.

EST is integrating and delivering a selection of algorithms on the Renesas autonomy platform by utilizing the dedicated on-chip accelerators providing high performance at low power consumption. In addition, EST provides training and consulting to Renesas partners and customers to enable the optimal use of the solutions in their target applications.

Eric Pinton, director at Renesas' Global ADAS Center, said: "eSoftThings have gained in-depth know-how of our accelerators for sensing and cognitive applications. This helps us to train our customers and partners to implement their solutions effectively as well as shortening time-to-market. We value eSoftThings as an important partner for our Renesas autonomy Platform. Therefore, we are pleased that they are now officially member of the R-Car Consortium."

Related Content

  • Blip Systems and Traffic Data Systems partner on traffic management
    November 18, 2014
    Danish IT company Blip Systems has appointed German traffic monitoring specialist Traffic Data Systems as its value added reseller for German-speaking countries. Traffic Data Systems is now offering BlipTrack, a non-intrusive solution that collects, analyses and visualises real-time data. BlipTrack sensors collect data from passing vehicles equipped with Bluetooth and wi-fi-enabled devices and calculate journey times and traffic flow. The solution is also able to analyse data from third-party data source
    Read More
  • Smartphone solution for parking performance
    March 31, 2017
    Automated parking offers optimised space utilisation and fewer damage complaints as David Crawford discovers. As cars become smarter, technology designed to make parking them more straightforward is developing in parallel. In turn, it is becoming clear that the places where vehicles spend much of their time will need to respond – more comprehensively than by supporting established aids such as smartphone-based parking location and reservation, or payment for time used.
    Read More
  • Smartphone solution for parking performance
    March 31, 2017
    Automated parking offers optimised space utilisation and fewer damage complaints as David Crawford discovers. As cars become smarter, technology designed to make parking them more straightforward is developing in parallel. In turn, it is becoming clear that the places where vehicles spend much of their time will need to respond – more comprehensively than by supporting established aids such as smartphone-based parking location and reservation, or payment for time used.
    Read More
  • Health and care organisation adopt Spark EV AI-based technology
    March 7, 2018
    UK-based health and care organisation Provide has adopted Spark EV’s artificial intelligence-based technology with the intention of removing range anxiety for drivers in its electric vehicle (EV) fleet rollout. The technology is said to enable the cars to complete 20% more journeys between charges. Called Spark, the system collects live driver, vehicle and other data sources through an in-car sensor. It uses cloud-based machine learning algorithms to provide more accurate journey predictions for EVs.
    Read More

Highlighted Content