Multi-modal Large Language Models (MLLMs) have shown impressive abilities in
generating reasonable responses with respect to multi-modal contents. However, there is …

Connected and autonomous driving is developing rapidly in recent years. However, current
autonomous driving systems, which are primarily based on data-driven approaches, exhibit …

To ensure safe driving in dynamic environments, autonomous vehicles should possess the
capability to accurately predict the lane change intentions of surrounding vehicles in …

Following the success of Large Language Models (LLMs), Large Multimodal Models
(LMMs), such as the Flamingo model and its subsequent competitors, have started to …

Typically, autonomous driving adopts a modular design, which divides the full stack into
perception, prediction, planning and control parts. Though interpretable, such modular …

Current Multimodal Large Language Models (MLLMs) typically integrate a pre-trained LLM
with another pre-trained vision transformer through a connector, such as an MLP, endowing …

Existing datasets for autonomous driving (AD) often lack diversity and long-range
capabilities, focusing instead on 360* perception and temporal reasoning. To address this …

Motion prediction is among the most fundamental tasks in autonomous driving. Traditional
methods of motion forecasting primarily encode vector information of maps and historical …

Abstract Multi-modal Large Language Models (MLLMs) have demonstrated impressive
instruction abilities across various open-ended tasks. However previous methods have …

The Tactical Driver Behavior modeling problem requires an understanding of driver actions
in complicated urban scenarios from rich multimodal signals including video, LiDAR and …