Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for
training models with broad, general capabilities for text, images, and other modalities …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

To facilitate the monitoring and management of modern transportation systems, monocular
visual traffic surveillance systems have been widely adopted for speed measurement …

Simulation has the potential to massively scale evaluation of self-driving systems, enabling
rapid development as well as safe deployment. Bridging the gap between simulation and …

In order to plan a safe maneuver an autonomous vehicle must accurately perceive its
environment, and understand the interactions among traffic participants. In this paper, we …

Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

This paper introduces the deep coordination graph (DCG) for collaborative multi-agent
reinforcement learning. DCG strikes a flexible trade-off between representational capacity …

Over the past decades Machine Learning and Deep Learning algorithm played a vital part in
the development of Autonomous Vehicle. It is indeed for the perception system to examine …

In this work we present a simple end-to-end trainable machine learning system capable of
realistically simulating driving experiences. This can be used for verification of self-driving …

Simulation is a crucial tool for accelerating the development of autonomous vehicles.
Making simulation realistic requires models of the human road users who interact with such …