MAPLESS
AUTONOMOUS
DRIVING
Reimagine self-driving cars
IMAGRY’S AUTONOMOUS DRIVING PLATFORM
Imagry is building the world’s first mapless autonomous driving platform. Our approach fundamentally transforms autonomous mobility from research to commercialization by pushing the limits of Computer Vision and AI.
Today, the customary way to fully operate autonomous vehicles is to use HD mapping that requires a strong connection to satellite GPS and continuous effort to update the map. Imagry’s mapless autonomous driving platform eliminates the need for this costly and time-consuming technology. Our solution uses a highly intelligent, vision-based approach that doesn’t require HD mapping, allowing the market for autonomous vehicles to scale and commercialize more rapidly at less cost.
Imagry’s autonomous driving platform comprises the following layers:
MODEL PERCEPTION
ALEPH STAR
CAR CONTROLS
IMAGRY’S AUTONOMOUS DRIVING PLATFORM
Imagry’s affordable and scalable level 4/5 autonomous driving platform utilizes commoditized sensors without the need for HD GPS-based solution. The platform is uniquely designed for unseen routes and scenarios. Imagry’s autonomous driving platform is real-world tested and proven on roads, in Israel and in the US.
Imagry’s autonomous driving platform comprises of the following layers:
MODEL PERCEPTION
ALEPH STAR
CAR CONTROLS
STATE OF THE ART TECHNOLOGY
LEARNS FROM SCRATCH
Learning based on intention,
safety and survival
ORDER OF MAGNITUDE
Minimal resources
TRAINABLE
Data collected from simulator
AUTONOMOUS DRIVING 2.0: MAPLESS
Imagry developed its mapless platform a combination of Deep Reinforcement Learning algorithms and search tree methodologies. This approach allows us to generate a real-time driving policy efficiently, on very limited computational resources.
STATE OF THE ART TECHNOLOGY
LEARNS FROM SCRATCH
Learning based on intention,
safety and survival
ORDER OF MAGNITUDE
Minimal resources
TRAINABLE
Data collected from simulator
AUTONOMOUS DRIVING 2.0: MAPLESS
Imagry is developing AI Technology based on combination of Deep Reinforcement Learning algorithms and search tree methodologies. This approach allows us to generate real-time driving policy efficiently, on a very limited computational resources.
WE’RE RECRUITING
We are looking for INNOVATORS in Machine Learning, Simulation, Autonomy and Control.
WE’RE RECRUITING
We are looking for INNOVATORS in Machine Learning, Simulation, Autonomy and Control.