Ziyi Song

Baolin Liu, Zongyuan Yang, Pengfei Wang et al.

The goal of scene text image super-resolution is to reconstruct high-resolution text-line images from unrecognizable low-resolution inputs. The existing methods relying on the optimization of pixel-level loss tend to yield text edges that exhibit a notable degree of blurring, thereby exerting a substantial impact on both the readability and recognizability of the text. To address these issues, we propose TextDiff, the first diffusion-based framework tailored for scene text image super-resolution. It contains two modules: the Text Enhancement Module (TEM) and the Mask-Guided Residual Diffusion Module (MRD). The TEM generates an initial deblurred text image and a mask that encodes the spatial location of the text. The MRD is responsible for effectively sharpening the text edge by modeling the residuals between the ground-truth images and the initial deblurred images. Extensive experiments demonstrate that our TextDiff achieves state-of-the-art (SOTA) performance on public benchmark datasets and can improve the readability of scene text images. Moreover, our proposed MRD module is plug-and-play that effectively sharpens the text edges produced by SOTA methods. This enhancement not only improves the readability and recognizability of the results generated by SOTA methods but also does not require any additional joint training. Available Codes:https://github.com/Lenubolim/TextDiff.

Ziyi Song, Chen Xia, Chenbing Wang et al.

Autonomous driving holds transformative potential but remains fundamentally constrained by the limited perception and isolated decision-making with standalone intelligence. While recent multi-agent approaches introduce cooperation, they often focus merely on perception-level tasks, overlooking the alignment with downstream planning and control, or fall short in leveraging the full capacity of the recent emerging end-to-end autonomous driving. In this paper, we present UniMM-V2X, a novel end-to-end multi-agent framework that enables hierarchical cooperation across perception, prediction, and planning. At the core of our framework is a multi-level fusion strategy that unifies perception and prediction cooperation, allowing agents to share queries and reason cooperatively for consistent and safe decision-making. To adapt to diverse downstream tasks and further enhance the quality of multi-level fusion, we incorporate a Mixture-of-Experts (MoE) architecture to dynamically enhance the BEV representations. We further extend MoE into the decoder to better capture diverse motion patterns. Extensive experiments on the DAIR-V2X dataset demonstrate our approach achieves state-of-the-art (SOTA) performance with a 39.7% improvement in perception accuracy, a 7.2% reduction in prediction error, and a 33.2% improvement in planning performance compared with UniV2X, showcasing the strength of our MoE-enhanced multi-level cooperative paradigm.

Zhepu Xu, Ziyi Song, Yupu Dong et al.

As China's road network enters the maintenance era, work zones will become a common sight on the roads. With the development of automated driving, vehicles equipped with Level 2/3 automated driving capabilities will also become a common presence on the roads. When these vehicles pass through work zones, automated driving may disengage, which can have complex effects on traffic safety. This paper explores the impact of Level 2/3 automated driving technology on road safety in high-speed highway work zone environments. Through microscopic traffic simulation method and using full-type traffic conflict technique, factors such as market penetration rate (MPR), traffic volume level, disengagement threshold, and driver takeover style are studied to understand their impact on work zone safety. The study found that the impact of automated driving technology on work zone safety is complex. Disengagement of automated vehicles in work zones reduces the proportion of vehicles that can maintain automated driving status. If takeover is not timely or adequate, it can easily lead to new traffic conflicts. Different factors have varying degrees of impact on work zone safety. Increasing MPR helps reduce the occurrence of single-vehicle conflicts, but it also increases the possibility of multi-vehicle conflicts. Therefore, future research and improvement directions should focus on optimizing the disengagement detection and takeover mechanisms of automated driving systems.