In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …

Collision risk assessment aims to provide a subjective cognitive comprehension of the risk
level in driving scenarios, which is critical for the safety of autonomous driving systems …

Given the uncertainty surrounding how existing explainability methods for autonomous
vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is …

Understanding the road genome is essential to realize autonomous driving. This highly
intelligent problem contains two aspects-the connection relationship of lanes, and the …

Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is
challenging because of the difficulty of modeling the complex interaction relations among …

Autonomous driving, propelled by data-driven approaches, has made significant
advancements. However, challenges remain, particularly in achieving human-like cognitive …

Recently, beyond object detection and segmentation, high-level understanding of
autonomous driving scenarios is attracting increasing attention. And traffic scene graph has …

Abstract We present Egocentric Action Scene Graphs (EASGs) a new representation for long-
form understanding of egocentric videos. EASGs extend standard manually-annotated …

This work explores scene graphs as a distilled representation of high-level information for
autonomous driving, applied to future driver-action prediction. Given the scarcity and strong …

Spatial-temporal scene graphs (STSG) are emerging for motion prediction in autonomous
driving. Existing work focuses on the graph structure and corresponding graph neural …