Some may think that the greatest challenge to automating vehicles is in developing the algorithms that tell a vehicle where and how to drive – the planning and policy. It is not. The greatest challenge lies in sensing and perception, in building a perception system that can reliably create the most accurate and robust environmental model for the planning and policy functions to act upon. In this way, perception systems are fundamental to enabling higher levels of automation.
As OEMs look for the best perception systems to deploy in their vehicles to enable lifesaving, active safety capabilities, radar offers a multitude of benefits, including low system cost and resiliency through a wide range of weather and lighting conditions.
These attributes make radar an ideal foundation for building any vehicle’s environmental model, and they become especially critical as vehicles move beyond basic warning functions and into assistance and automation functions. Centralizing the intelligence and applying machine learning in just the right way can turbocharge the performance, ensuring that vehicles capitalize on radar’s strengths while fusing its data with that of other sensing modalities. In doing so, OEMs can create the best canvas on which to design and implement planning and policy functions that provide advanced features and solve the most challenging corner cases.
In this white paper, learn how machine learning can make the most of the data collected by radar and other sensing modalities and see specific examples of this technology in action.
