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How to make automated driving safe?

HD maps for autonomous driving rely on a large amount of data, collected through sensors such as LiDAR, GPS, IMU, and radars, placed on autonomous vehicles (AV). This data must be annotated and curated accurately to allow safe navigation through autonomous vehicles. This task is challenging.

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The localization of HD maps for self-driving cars is another significant challenge that is restricting the industry to realize the AV dream.

Challenges in HD Mapping

Gartner forecasts that by 2023, over 740,000 autonomous vehicles will make a presence in the market. However, for the AVs to be quickly and effectively deployed on the roads, some challenges need to be conquered first.

Petabytes of Data

Autonomous vehicles collect data from a variety of sources such as cameras and sensors, including imagery, LiDAR, radar, GPS. This raw data must be annotated and labeled for making autonomous vehicles self-reliant. It is quite a challenging task to process the petabytes of data collected from such a wide variety of sources effectively. Traditional architectures of automotive frameworks are not found suitable for the large-scale data processing workloads required for testing the algorithms used in the testing of autonomous vehicles. This massive amount of data must be analyzed intelligently so as to enable autonomous vehicles to make decisions faster in diverse conditions. This is necessary for achieving efficient HD mapping solutions, which are necessary for making safe driving through AVs a reality.

Localization of Maps

Additionally, for safe navigation, it is necessary to associate the static map data with detected dynamic information. This is called localization of maps which is an essential component of autonomous driving.

Maps represent the static world, however, some parts of the world keep on changing frequently. HD maps need to be updated accordingly so that the automated vehicles are able to drive safely by relying only on correct information about the roads. Updated HD maps help the AVs to locate and identify lane markings, static and moving objects, pedestrians, traffic lights, signages, etc., thereby helping them to make sense of their surroundings and drive safely.

Scaling up with the Right Partner 

Autonomous vehicles are still working towards reaching the stage of full autonomy. There are a lot of issues to be ironed out; safety being the foremost. To be able to effectively detect, track and classify objects and make informed decisions for path planning and safe navigation, the autonomous industry needs to establish the right partnerships. 

The industry needs to work with experts in the field of HD Mapping Solutions, Data Annotation, 3D Modelling, 3D Image Capture, etc. with a strong portfolio of global projects to make the dream a reality. 

Extensive mapping and 3D modeling experience along with a solid leadership team make us qualified to be such an expert.  Our team possesses the experience and expertise to create market-leading HD maps for autonomous driving that can significantly enhance sensor perception, enable precise localization, and improve path planning for AVs. Our analysts specialize in analyzing the sensory data collected by autonomous vehicles from disparate sources. 

AI systems can aid in cleaning up the data collected by autonomous vehicles. But the precision level achieved by these systems is about 70%-80%. For achieving 100% precision in the data, human expertise is indispensable. This is where our strength lies. We are well-equipped to provide you with expert HD mapping services.

Along with expertise in mapping, our extensive experience in 3D modeling provides us an added advantage of understanding the driving environment intricately. Breaking the silos, we are also partnering with leading mapmakers and leaders in providing large-scale and systematic visualization of physical environments; all this to enable the autonomous vehicles industry to achieve its goals, most effectively and quickly.

Conclusion

To win the autonomous vehicles race, automakers have to move from developing autonomy to developing an intelligent autonomy. Automakers who will be able to accelerate this process will gain a winning edge. 

Be it enabling the AVs to locate objects and boundaries like people, cars, flowers, furniture, or human faces or to safely navigate using highly accurate HD maps for self-driving cars, our experts are ready to smoothen the journey for the industry. As we work towards creating unique ‘glocal’ maps for autonomous vehicles, we look forward to exploring the various opportunities in HD mapping and collaborating and evolving together.

Also Read: Spatial Intelligence is at the Centre Stage

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