Skip to main content

Mobileye and Delphi partner on SAE Level 4/5 automated driving solution

Computer vision systems specialist Mobileye and Delphi Automotive, which specialises in automated driving software, are to collaborate to develop a complete SAE Level 4/5 automated driving solution. The solution will be based on key technologies from each company, including Mobileye's EyeQ 4/5 system on a chip (SoC) with sensor signal processing, fusion, world view generation and Road Experience Management (REM) system, which will be used for real time mapping and vehicle localisation. Delphi will inc
August 23, 2016 Read time: 2 mins
Computer vision systems specialist 4279 Mobileye and 7207 Delphi Automotive, which specialises in automated driving software, are to collaborate to develop a complete SAE Level 4/5 automated driving solution.

The solution will be based on key technologies from each company, including Mobileye's EyeQ 4/5 system on a chip (SoC) with sensor signal processing, fusion, world view generation and Road Experience Management (REM) system, which will be used for real time mapping and vehicle localisation.

Delphi will incorporate automated driving software algorithms from its Ottomatika acquisition, which include the path and motion planning features, and Delphi's Multi-Domain Controller (MDC) with the full camera, radar and LiDAR suite.

In addition, teams from both companies will develop the next generation of sensor fusion technology as well as the next generation human-like ‘driving policy’.  This module combines Ottomatika's driving behaviour modelling with Mobileye's deep reinforcement learning in order to yield driving capabilities necessary for negotiating with other human drivers and pedestrians in complex urban scenes.

For more information on companies in this article

Related Content

  • Aimsun takes part in driver data study to improve C/AVs
    November 14, 2018
    Aimsun is taking part in a UK study which is using human driver data to help improve the performance and acceptability of connected and autonomous vehicles (C/AVs). The one-year project, Learning through Ambient Driving Styles for Autonomous Vehicles (LAMBDA-V), will also look at how driver behaviour can be analysed and used to accelerate the adoption of C/AVs. Aimsun says new rules for safer and more efficient driving behaviour could be created from existing vehicles, based on road laws and on how h
  • Indra drones to manage road traffic in Spain
    October 14, 2019
    Indra is to use drones to monitor road traffic and detect incidents in Lugo, Spain. The company plans to employ the drones as sensors for current transportation monitoring systems and integrate them into its transportation control solution Mova Traffic. It will also develop tools to analyse video and images taken by drones in a bid to detect incidents automatically. Additionally, the company will incorporate its drones with a transportation control centre, which will process real-time image and video tra
  • Traffic management: risky business
    June 15, 2023
    Adding a real-time accident risk layer to the profile of a road network ticks all the crucial boxes: it saves time, fuel, money and, ultimately, lives. Harriet King of Valerann explains the brain power of Lanternn by Valerann’s Core Fusion Engine...
  • MIT study combines traffic data for smarter signal timings
    April 1, 2015
    Researchers at Massachusetts Institute of Technology (MIT) have found a method of combining vehicle-level data with less precise, but more comprehensive, city-level data on traffic patterns to produce better information than current systems provide. They claim this reduce delays, improve efficiency, and reduce emissions. The new findings are reported in a pair of papers by assistant professor of civil and environmental engineering Carolina Osorio and alumna Kanchana Nanduri, published in the journals Tra