Search Results for author:
Found 11 papers, 6 papers with code
OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model
We present OpenDriveVLA, a Vision-Language Action (VLA) model designed for end-to-end autonomous driving.
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
We present TUMTraffic-VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios.
WARM-3D: A Weakly-Supervised Sim2Real Domain Adaptation Framework for Roadside Monocular 3D Object Detection
Exploring the use of cost-effective, extensive synthetic datasets offers a viable solution to tackle this challenge and enhance the performance of roadside monocular 3D detection.
GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs
In this study, we introduce an object relation module, consisting of a graph generator and a graph neural network (GNN), to learn the spatial information from certain patterns to improve 3D object detection.
PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems
We achieve a frame rate of 10 FPS while keeping compression sizes below 105 Kb, a reduction of 50 times, and maintaining object detection performance on par with the original data.
TUMTraf V2X Cooperative Perception Dataset
We propose CoopDet3D, a cooperative multi-modal fusion model, and TUMTraf-V2X, a perception dataset, for the cooperative 3D object detection and tracking task.
GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events
The recognition and understanding of traffic incidents, particularly traffic accidents, is a topic of paramount importance in the realm of intelligent transportation systems and intelligent vehicles.
A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook
Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques.
Vision Language Models in Autonomous Driving: A Survey and Outlook
The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD) have attracted widespread attention due to their outstanding performance and the ability to leverage Large Language Models (LLMs).
Real-Time And Robust 3D Object Detection with Roadside LiDARs
This work aims to address the challenges in autonomous driving by focusing on the 3D perception of the environment using roadside LiDARs.
A Survey of Robust 3D Object Detection Methods in Point Clouds
The purpose of this work is to review the state-of-the-art LiDAR-based 3D object detection methods, datasets, and challenges.
