Drones Learn Autonomous Flying by Imitating Cars and Bikes
The algorithm DroNet allows drones to fly completely by themselves through the streets of a city and in indoor environments. It produces two outputs for each single input image: a steering angle to keep the drone navigating while avoiding obstacles, and a collision probability to let the drone recognize dangerous situations and promptly react to them.
The algorithm learns to solve complex tasks from a set of ‘training examples’ that shows the drone how to do certain things and cope with difficult situations, much like children learn from their parents or teachers. In this case, cars and bikes are the drones’ teachers.
Top Stories
INSIDERAerospace
New Clean Planet Facility Converts Waste Plastic to Sustainable Aviation Fuel
INSIDERMaterials
Researchers Discover Material That Conducts Heat Better Than Copper
NewsManned Systems
Downstream Take on Electric Construction Vehicles
NewsPower
Mercedes Sticks with EVs After Making a Few Adjustments
NewsGovernment
Forvia Hella Ready with ADB; NHTSA: Not So Fast
INSIDERAerospace
New Study Finds Lean-Burn Engines Don’t Reduce Aircraft Contrail Formation
Webcasts
Automotive
Virtual. Physical. Connected: How Smart Testing Is Changing Automotive...
Energy
Battery Manufacturing & Simulation Summit 2026
Power
Virtual Screening of Materials for Increased Battery Performance
Software
Scaling SDV Development with Virtualization
Defense
High-Speed Connectivity for Next Generation Aerospace & Defense...
Electronics & Computers
Electronics Digital Twins: From Concept to Scalable Platform



