Personalized Federated Learning of Driver Prediction Models for Autonomous Driving

no code implementations • 2 Dec 2021 • Manabu Nakanoya, Junha Im, Hang Qiu, Sachin Katti, Marco Pavone, Sandeep Chinchali

Autonomous vehicles (AVs) must interact with a diverse set of human drivers in heterogeneous geographic areas.

Autonomous Driving Personalized Federated Learning +1

Sampling Training Data for Continual Learning Between Robots and the Cloud

no code implementations • 12 Dec 2020 • Sandeep Chinchali, Evgenya Pergament, Manabu Nakanoya, Eyal Cidon, Edward Zhang, Dinesh Bharadia, Marco Pavone, Sachin Katti

Today's robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data, such as rare scenes of road construction sites, to steadily improve robotic perception models.

Cloud Computing Continual Learning +2

Task-relevant Representation Learning for Networked Robotic Perception

no code implementations • 6 Nov 2020 • Manabu Nakanoya, Sandeep Chinchali, Alexandros Anemogiannis, Akul Datta, Sachin Katti, Marco Pavone

However, today's representations for sensory data are mostly designed for human, not robotic, perception and thus often waste precious compute or wireless network resources to transmit unimportant parts of a scene that are unnecessary for a high-level robotic task.

Motion Planning Representation Learning