Intersections are common roadway features, whether four-way stops in a neighborhood or traffic-light-filled exchanges on busy multi-lane thoroughfares.
Given the frequency, variety and risk associated with intersections — more than 50 percent of serious accidents in the U.S. happen at or near them — it’s critical that an autonomous vehicle be able to accurately navigate them.
Handling intersections autonomously presents a complex set of challenges for self-driving cars. This includes the ability to stop accurately at an intersection wait line or crosswalk, correctly process and interpret right of way traffic rules in various scenarios, and determine and execute the correct path for a variety of maneuvers, such as proceeding straight through the intersection and unprotected intersection turns.
Earlier in the DRIVE Labs series, we demonstrated how we detect intersections, traffic lights, and traffic signs with the WaitNet DNN. And how we classify traffic light state and traffic sign type with the LightNet and SignNet DNNs. In this episode, we go further to show how NVIDIA uses AI to perceive the variety of intersection structures that an autonomous vehicle could encounter on a daily drive.
Read the full blog, At a Crossroads: How AI Helps Autonomous Vehicles Understand Intersections, on the NVIDIA Blog.