We study how vision-language models (VLMs) trained on web-scale data can be integrated
into end-to-end driving systems to boost generalization and enable interactivity with human …

We present DrivingGaussian an efficient and effective framework for surrounding dynamic
autonomous driving scenes. For complex scenes with moving objects we first sequentially …

Recently the rise of query-based Transformer decoders is resha** camera-based 3D
object detection. These query-based decoders are surpassing the traditional dense BEV …

Domain shifts such as sensor type changes and geographical situation variations are
prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on …

The reliability of driving perception systems under unprecedented conditions is crucial for
practical usage. Latest advancements have prompted increasing interest in multi-LiDAR …

Despite significant advancements in vision-language models (VLMs), there lacks effective
approaches to enhance response quality by scaling inference-time computation. This …

High-level task planning under adversarial environments is one of the central problems in
the development of autonomous systems such as unmanned ground vehicles (UGV) …

Vehicle Re-identification (Re-ID) has been broadly studied in the last decade; however, the
different camera view angles leading to confused discrimination in the feature subspace for …

AI applications are becoming increasingly visible to the general public. There is a notable
gap between the theoretical assumptions researchers make about computer vision models …

Prevailing wisdom asserts that one cannot rely on the cloud for critical real-time control
systems like self-driving cars. We argue that we can, and must. Following the trends of …