Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

With the emergence of Large Language Models (LLMs) and Vision Foundation Models
(VFMs), multimodal AI systems benefiting from large models have the potential to equally …

Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …

Evaluating and improving planning for autonomous vehicles requires scalable generation of
long-tail traffic scenarios. To be useful, these scenarios must be realistic and challenging …

Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe
driving behaviors. While many prior works aim to achieve higher prediction accuracy, few …

As self-driving systems become better, simulating scenarios where the autonomy stack may
fail becomes more important. Traditionally, those scenarios are generated for a few scenes …

Simulators offer the possibility of safe, low-cost development of self-driving systems.
However, current driving simulators exhibit naïve behavior models for background traffic …

Autonomous driving techniques have been flourishing in recent years while thirsting for
huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up …