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20232026
most citedVision Language Models in Autonomous Driving: A Survey and Outlook

17 citations · 33 across the 17 of their papers we have counts for

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18 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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.…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…