No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …

In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

We introduce Argoverse 2 (AV2)-a collection of three datasets for perception and forecasting
research in the self-driving domain. The annotated Sensor Dataset contains 1,000 …

Autonomous driving is regarded as one of the most promising remedies to shield human
beings from severe crashes. To this end, 3D object detection serves as the core basis of …

Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of
surrounding traffic participants can significantly improve decision-making and safety in …

Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Motivated by the impact of large-scale datasets on ML systems we present the largest self-
driving dataset for motion prediction to date, containing over 1,000 hours of data. This was …

Current perception models in autonomous driving have become notorious for greatly relying
on a mass of annotated data to cover unseen cases and address the long-tail problem. On …

Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very
challenging due to complex interactions between pedestrians. However, previous works …

Understanding crowd motion dynamics is critical to real-world applications, eg, surveillance
systems and autonomous driving. This is challenging because it requires effectively …