Accurate pedestrian trajectory prediction is crucial for various applications, and it requires a
deep understanding of pedestrian motion patterns in dynamic environments. However …

Various methods have been proposed for utilizing Large Language Models (LLMs) in
autonomous driving. One strategy of using LLMs for autonomous driving involves inputting …

Due to the limitations of network communication conditions for online calling GPT, the
onboard deployment of Large Language Models for autonomous driving is in need. In this …

Real-world autonomous driving systems must make safe decisions in the face of rare and
diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world …

Traffic congestion in metropolitan areas presents a formidable challenge with far-reaching
economic, environmental, and societal ramifications. Therefore, effective congestion …

Autonomous driving has advanced significantly due to sensors, machine learning, and
artificial intelligence improvements. However, prevailing methods struggle with intricate …

Large Language Models (LLMs) are demonstrating outstanding potential for tasks such as
text generation, summarization, and classification. Given that such models are trained on a …

With computer technology advancing in both software and hardware, the benefits of
embodied intelligence are becoming increasingly evident. This robust interactive learning …

Understanding the evolution of 3D scenes is important for effective autonomous driving.
While conventional methods mode scene development with the motion of individual …

The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing
development of highly autonomous vehicles, largely due to advances in deep learning, the …