Xiaofei Mao

Weicheng Zheng, Xiaofei Mao, Nanfei Ye et al.

The advent of Vision-Language Models (VLMs) has significantly advanced end-to-end autonomous driving, demonstrating powerful reasoning abilities for high-level behavior planning tasks. However, existing methods are often constrained by a passive perception paradigm, relying solely on text-based reasoning. This passivity restricts the model's capacity to actively seek crucial visual evidence when faced with uncertainty. To address this, we introduce DriveAgent-R1, the first autonomous driving agent capable of active perception for planning. In complex scenarios, DriveAgent-R1 proactively invokes tools to perform visual reasoning, firmly grounding its decisions in visual evidence, thereby enhancing both interpretability and reliability. Furthermore, we propose a hybrid thinking framework, inspired by human driver cognitive patterns, allowing the agent to adaptively switch between efficient text-only reasoning and robust tool-augmented visual reasoning based on scene complexity. This capability is cultivated through a three-stage progressive training strategy, featuring a core Cascaded Reinforcement Learning (Cascaded RL) phase. Extensive experiments on the Drive-Internal dataset, which is rich in long-tail scenarios, and the public nuScenes dataset show that, with only 3B parameters, DriveAgent-R1 achieves competitive performance comparable to top closed model systems such as GPT-5 and to human driving proficiency while remaining deployment-friendly, offering a proven path toward building more intelligent autonomous driving systems.

Xiaofei Mao, Jiahao Cao, Dongfang Li et al.

Holistic person re-identification (Re-ID) and partial person re-identification have achieved great progress respectively in recent years. However, scenarios in reality often include both holistic and partial pedestrian images, which makes single holistic or partial person Re-ID hard to work. In this paper, we propose a robust coarse granularity part-level person Re-ID network (CGPN), which not only extracts robust regional level body features, but also integrates supervised global features for both holistic and partial person images. CGPN gains two-fold benefit toward higher accuracy for person Re-ID. On one hand, CGPN learns to extract effective body part features for both holistic and partial person images. On the other hand, compared with extracting global features directly by backbone network, CGPN learns to extract more accurate global features with a supervision strategy. The single model trained on three Re-ID datasets including Market-1501, DukeMTMC-reID and CUHK03 achieves state-of-the-art performances and outperforms any existing approaches. Especially on CUHK03, which is the most challenging dataset for person Re-ID, in single query mode, we obtain a top result of Rank-1/mAP=87.1\%/83.6\% with this method without re-ranking, outperforming the current best method by +7.0\%/+6.7\%.