17 citations · 33 across the 17 of their papers we have counts for
19 papers
ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes
Rui Song, Tianhui Cai, Markus Gross +5
Feedforward 3D Gaussian Splatting (3DGS) often struggles in trajectory-based sparse-view driving scenes. Existing Gaussian repair methods mainly target optimization-based 3DGS, whi…
CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs
Xingcheng Zhou, Hao Guo, Rui Song +5
Safety-critical traffic reasoning requires contrastive consistency: models must detect true hazards when an accident occurs, and reliably reject plausible-but-false hypotheses unde…
SGTA: Scene-Graph Based Multi-Modal Traffic Agent for Video Understanding
Xingcheng Zhou, Mingyu Liu, Walter Zimmer +2
We present Scene-Graph Based Multi-Modal Traffic Agent (SGTA), a modular framework for traffic video understanding that combines structured scene graphs with multi-modal reasoning.…
SegRGB-X: General RGB-X Semantic Segmentation Model
Jiong Liu, Yingjie Xu, Xingcheng Zhou +4
Semantic segmentation across arbitrary sensor modalities faces significant challenges due to diverse sensor characteristics, and the traditional configurations for this task result…
TUMTraf EMOT: Event-Based Multi-Object Tracking Dataset and Baseline for Traffic Scenarios
Mengyu Li, Xingcheng Zhou, Guang Chen +2
In Intelligent Transportation Systems (ITS), multi-object tracking is primarily based on frame-based cameras. However, these cameras tend to perform poorly under dim lighting and h…
Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset
Walter Zimmer, Ross Greer, Xingcheng Zhou +7
Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as an unavoidable…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model
Xingcheng Zhou, Xuyuan Han, Feng Yang +3
We present OpenDriveVLA, a Vision Language Action model designed for end-to-end autonomous driving, built upon open-source large language models. OpenDriveVLA generates spatially g…
Towards Vision Zero: The TUM Traffic Accid3nD Dataset
Walter Zimmer, Ross Greer, Daniel Lehmberg +9
Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as unavoidable an…
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
Xingcheng Zhou, Konstantinos Larintzakis, Hao Guo +7
We present TUMTraffic-VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios. The dataset comprises 1,000 vid…
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors
Walter Zimmer, Ross Greer, Xingcheng Zhou +7
Road traffic injuries are the leading cause of death for people aged 5-29, resulting in about 1.19 million deaths each year. To reduce these fatalities, it is essential to address…
Car-GS: Addressing Reflective and Transparent Surface Challenges in 3D Car Reconstruction
Congcong Li, Jin Wang, Xiaomeng Wang +4
3D car modeling is crucial for applications in autonomous driving systems, virtual and augmented reality, and gaming. However, due to the distinctive properties of cars, such as hi…
WARM-3D: A Weakly-Supervised Sim2Real Domain Adaptation Framework for Roadside Monocular 3D Object Detection
Xingcheng Zhou, Deyu Fu, Walter Zimmer +4
Existing roadside perception systems are limited by the absence of publicly available, large-scale, high-quality 3D datasets. Exploring the use of cost-effective, extensive synthet…
GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs
Mingyu Liu, Ekim Yurtsever, Marc Brede +6
Accurate and effective 3D object detection is critical for ensuring the driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D object detectors have exhibit…
PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems
Walter Zimmer, Ramandika Pranamulia, Xingcheng Zhou +2
In the context of Intelligent Transportation Systems (ITS), efficient data compression is crucial for managing large-scale point cloud data acquired by roadside LiDAR sensors. The…
TUMTraf V2X Cooperative Perception Dataset
Walter Zimmer, Gerhard Arya Wardana, Suren Sritharan +3
Cooperative perception offers several benefits for enhancing the capabilities of autonomous vehicles and improving road safety. Using roadside sensors in addition to onboard sensor…
GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events
Xingcheng Zhou, Alois C. Knoll
The recognition and understanding of traffic incidents, particularly traffic accidents, is a topic of paramount importance in the realm of intelligent transportation systems and in…
A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook
Mingyu Liu, Ekim Yurtsever, Jonathan Fossaert +5
Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques. High-quality datasets are fundamental for…
Vision Language Models in Autonomous Driving: A Survey and Outlook
Xingcheng Zhou, Mingyu Liu, Ekim Yurtsever +4
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 t…