Congruent
ActiveAI native radars for self-driving cars
About
At Congruent, we build radars for end-to-end autonomous systems. The most advanced autonomous systems are trained as a single neural network from raw sensor data to navigation actions. For a sensor to be included in these pipelines sensor stacks requires two key properties: access to raw sensor data and a high-fidelity sensor simulator. Current automotive radars have neither, they output heavily processed point clouds and no raw radar simulator exists for driving scenes. Congruent solves both problems: a radar architecture that exposes raw data, paired with a world model based radar simulator. Radar is the only depth sensor at a price point that scales to every car on the road and works in all weather conditions. Congruent is building the radar compatible with the training architectures that will make mass-market vehicles autonomous.
Founders
Clement Barthes· Co-Founderex-head of autonomy at Zendar ex-CTO at Safehub, making smart sensors to evaluate building damage after earthquakes ex-Research Engineer and Lab Manager at UC Berkeley - PEER lab
Evan Carnahan· Co-FounderCo-Founder @ Congruent | Machine learning researcher with a deep background in signal processing and sensor fusion. Compulsive generalist and deeply curious about all things sensing and learning.
Product launches · 1 launch
The radar to unlock mass-market self-driving cars