In current years, the improvement of deep learning has brought about tremendous changes:
As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) …

Autonomous driving (AD) has developed tremendously in parallel with the ongoing
development and improvement of deep learning (DL) technology. However, the uptake of …

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

Problems such as equipment defects or limited viewpoints will lead the captured point
clouds to be incomplete. Therefore, recovering the complete point clouds from the partial …

Abstract We present ChatScene a Large Language Model (LLM)-based agent that
leverages the capabilities of LLMs to generate safety-critical scenarios for autonomous …

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 …

The prevailing way to test a self-driving vehicle (SDV) in simulation involves non-reactive
open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the …

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 …

For safe motion planning in real-world autonomous vehicles require behavior prediction
models that are reliable and robust to distribution shifts. The recent studies suggest that the …

Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling
accident-prone traffic events by algorithm designs at the policy level, we investigate a …