4 citations · 4 across the 11 of their papers we have counts for
13 papers
AffordDrive3D: Affordance-Aware World-Action Modeling with Spatial Understanding
Tianhui Cai, Xinglong Sun, Chao Fang +6
World-action models have recently improved autonomous driving by jointly learning future scene prediction and trajectory generation. Most existing approaches model the future prima…
Semantic-Aware, Physics-Informed, Geometry-Grounded Weather Video Synthesis
Chenghao Qian, Nedko Savov, Lingdong Kong +7
Weather synthesis aims to add weather effects to input videos while preserving scene identity, structure, and motion. The key limitation of existing methods is the lack of diversit…
nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving
Zhiyu Huang, Johnson Liu, Rui Song +13
Reasoning is essential for autonomous driving (AD) in long-tail scenarios, where vehicles must apply commonsense knowledge, understand spatial relations, infer agent interactions,…
MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
Marco Coscoy, Zewei Zhou, Seth Z. Zhao +9
Vehicle-to-Everything (V2X) communication has emerged as a promising paradigm for autonomous driving, enabling connected agents to share complementary perception information and ne…
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…
EnerGS: Energy-Based Gaussian Splatting with Partial Geometric Priors
Rui Song, Tianhui Cai, Markus Gross +5
3D Gaussian Splatting (3DGS) has been widely adopted for scene reconstruction, where training inherently constitutes a highly coupled and non-convex optimization problem. Recent wo…
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…
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…
Energy-Aware Imitation Learning for Steering Prediction Using Events and Frames
Hu Cao, Jiong Liu, Xingzhuo Yan +5
In autonomous driving, relying solely on frame-based cameras can lead to inaccuracies caused by factors like long exposure times, high-speed motion, and challenging lighting condit…
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…
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…
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…
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…