In real-world traffic scenarios, agents such as pedestrians and car drivers often observe
neighboring agents who exhibit similar behavior as examples and then mimic their actions …

Motion prediction and planning are key components to enable autonomous driving.
Although high definition (HD) maps provide important contextual information that constrains …

Lane graph estimation is a long-standing problem in the context of autonomous driving.
Previous works aimed at solving this problem by relying on large-scale, hand-annotated …

Trajectory prediction is a challenging problem that requires considering interactions among
multiple actors and the surrounding environment. While data-driven approaches have been …

Trajectory prediction is an important task in autonomous driving. State-of-the-art trajectory
prediction models often use attention mechanisms to model the interaction between agents …

The\ac {CVAE} is one of the most widely-used models in trajectory prediction for\ac {AD}. It
captures the interplay between a driving context and its ground-truth future into a …

We propose a post hoc saliency-based explanation framework for counterfactual reasoning
in probabilistic multivariate time-series forecasting (regression) settings. Building upon …

Forecasting the scalable future states of surrounding traffic participants in complex traffic
scenarios is a critical capability for autonomous vehicles, as it enables safe and feasible …

Precisely predicting the future trajectories of surrounding traffic participants is a crucial but
challenging problem in autonomous driving, due to complex interactions between traffic …

Understanding road geometry is a critical component of the autonomous vehicle (AV) stack.
While high-definition (HD) maps can readily provide such information, they suffer from high …