Toyota enters partnership to build HD maps for AVs from space

Toyota Research Institute-Advanced Development (TRI-AD), technology company Maxar Technologies and NTT Data are working together to build high-definition (HD) maps for autonomous vehicles (AV) using satellite imagery. TRI-AD carried out an analysis, saying that current HD maps cover less than 1% of the global road network and there is a need to broaden the coverage of urban areas and local roads before AVs can become a mainstream mobility technology. A HD map created from satellite imagery would all
May 3, 2019 Read time: 2 mins
1686 Toyota Research Institute-Advanced Development (TRI-AD), technology company Maxar Technologies and NTT Data are working together to build high-definition (HD) maps for autonomous vehicles (AV) using satellite imagery.


TRI-AD carried out an analysis, saying that current HD maps cover less than 1% of the global road network and there is a need to broaden the coverage of urban areas and local roads before AVs can become a mainstream mobility technology.

A HD map created from satellite imagery would allow the driving software to compare multiple data sources and signal the car to take action to stay safe, the company adds.

Utilising Maxar’s Geospatial Big Data Platform, imagery from the company’s satellite imagery library will be fed into NTT Data’s specialised algorithms using artificial intelligence to extract information to generate the road network. TRI-AD will then make HD maps available for delivery from its cloud into Toyota test vehicles.

Mandali Khalesi, vice president of automated driving at TRI-AD, says advances in electronics and aerospace engineering are leading to higher resolutions and more frequent updates of global imagery from space-based assets.

“Additionally, machine learning is helping automate the discovery and integration of semantic relationships between road elements within image data,” Khalesi adds.

NTT Data, an IT services provider, will use its artificial intelligence and enhanced image-processing resources to expand the coverage of HD maps.

Katsuichi Sonoda, vice president and head of NTT Data’s social infrastructure solution sector, says: “In the future, we hope to map worldwide road networks from space using our enhanced image-processing expertise.”

Location Based Systems

Related Content

  • Vaisala's RoadAI can optimise maintenance
    August 20, 2019
    Alerts for natural disasters are ones that most of us would rather do without, writes Adam Hill. But the ITS industry still needs help to deal with more common meteorological issues Google Maps has added SOS alerts to its service. For those of us more used to using the phone app to navigate from a metro station to an unfamiliar restaurant, this may seem extreme. But this is not what Google has in mind. Its SOS messages are for “hurricane forecast cones, earthquake shake-maps and flood forecasts”. That
    Read More
  • Ambarella puts AI tech into Motional AVs
    March 12, 2021
    Ambarella says AI processors work with autonomous vehicles' Lidar, camera and radar sensors
    Read More
  • SafeRide: it’s time to act on cyberattacks
    May 10, 2019
    Cyber threats are increasing rapidly and conventional security measures are unable to keep up. Ben Spencer talks to SafeRide’s Gil Reiter about what OEMs can do now As more vehicles become connected, so the potential threats to their security increase. Gil Reiter, vice president of product management for security firm SafeRide, says the biggest ‘attack surface’ for connected cars is their internet connectivity - and the in-vehicle applications that use the internet connection. “The most vulnerable co
    Read More
  • Machine vision - cameras for intelligent traffic management
    January 25, 2012
    For some, machine vision is the coming technology. For others, it’s already here. Although it remains a relative newcomer to the ITS sector, its effects look set to be profound and far-reaching. Encapsulating in just a few short words the distinguishing features of complex technologies and their operating concepts can sometimes be difficult. Often, it is the most subtle of nuances which are both the most important and yet also the most easily lost. Happily, in the case of machine vision this isn’t the case:
    Read More

Highlighted Content