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Peng Jia

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4 papers

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AAAI Conference 2026 Conference Paper

Other Vehicle Trajectories Are Also Needed: A Driving World Model Unifies Ego-Other Vehicle Trajectories in Video Latent Space

Jian Zhu
Zhengyu Jia
Tian Gao
Jiaxin Deng
Shidi Li
Lang Zhang
Fu Liu
Peng Jia

Advanced end-to-end autonomous driving systems predict other vehicles' motions and plan ego vehicle's trajectory. The world model that can foresee the outcome of the trajectory has been used to evaluate the end-to-end autonomous driving system. However, existing world models predominantly emphasize the trajectory of the ego vehicle and leave other vehicles uncontrollable. This limitation hinders their ability to realistically simulate the interaction between the ego vehicle and the driving scenario. In addition, it remains a challenge to match multiple trajectories with each vehicle in the video to control the video generation. To address above issues, a driving World Model named EOT-WM is proposed in this paper, unifying Ego-Other vehicle Trajectories in videos. Specifically, we first project ego and other vehicle trajectories in the BEV space into the image coordinate to match each trajectory with its corresponding vehicle in the video. Then, trajectory videos are encoded by the Spatial-Temporal Variational Auto Encoder to align with driving video latents spatially and temporally in the unified visual space. A trajectory-injected diffusion Transformer is further designed to denoise the noisy video latents for video generation with the guidance of ego-other vehicle trajectories. In addition, we propose a metric based on control latent similarity to evaluate the controllability of trajectories. Extensive experiments are conducted on the nuScenes dataset, and the proposed model outperforms the state-of-the-art method by 30% in FID and 55% in FVD. The model can also predict unseen driving scenes with self-produced trajectories.

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EAAI Journal 2025 Journal Article

A decomposition–integration interval prediction strategy for iron ore shipping freight rates with reinforcement learning

Hongyue Guo
Yijia Zhang
Yating Yu
Lidong Wang
Peng Jia
Witold Pedrycz

The fluctuations in iron ore shipping freight rates significantly affect market participants’ investment decisions. This study proposes a “decomposition–integration” interval prediction method for iron ore shipping freight rates. First, the trend, seasonal, and residual components of the original data are extracted. Then, the residual component with high-frequency fluctuations is predicted by developing four interval prediction models with influencing factors. Reinforcement learning-derived dynamic weighting integrates prediction outputs to reveal the underlying data relationships. Finally, with point prediction outputs on the trend and seasonal components, the interval prediction is achieved by integrating these components predictions. The empirical results illustrate that the proposed method outperforms several benchmark methods in terms of prediction interval coverage probability of 61. 29% and deviation of 0. 02428, indicating its effectiveness in interval prediction.

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AAAI Conference 2025 Conference Paper

BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving

Tao Tang
Dafeng Wei
Zhengyu Jia
Tian Gao
Changwei Cai
Chengkai Hou
Peng Jia
Kun Zhan

The rapid development of the autonomous driving industry has led to a significant accumulation of autonomous driving data. Consequently, there comes a growing demand for retrieving data to provide specialized optimization. However, directly applying previous image retrieval methods faces several challenges, such as the lack of global feature representation and inadequate text retrieval ability for complex driving scenes. To address these issues, firstly, we propose the BEV-TSR framework which leverages descriptive text as an input to retrieve corresponding scenes in the Bird’s Eye View (BEV) space. Then to facilitate complex scene retrieval with extensive text descriptions, we employ a large language model (LLM) to extract the semantic features of the text inputs and incorporate knowledge graph embeddings to enhance the semantic richness of the language embedding. To achieve feature alignment between the BEV feature and language embedding, we propose Shared Cross-modal Embedding with a set of shared learnable embeddings to bridge the gap between these two modalities, and employ a caption generation task to further enhance the alignment. Furthermore, there lack of well-formed retrieval datasets for effective evaluation. To this end, we establish a multi-level retrieval dataset, nuScenes-Retrieval, based on the widely adopted nuScenes dataset. Experimental results on the multi-level nuScenes-Retrieval show that BEV-TSR achieves state-of-the-art performance, e.g., 85.78% and 87.66% top-1 accuracy on scene-to-test and text-to-scene retrieval respectively.

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EAAI Journal 2021 Journal Article

HK–SEIR model of public opinion evolution based on communication factors

Qing Li
YaJun Du
ZhaoYan Li
JinRong Hu
RuiLin Hu
BingYan Lv
Peng Jia

Microblog, with its good interaction and convenient dissemination, has become the main platform for public opinion dissemination. How to discover the law of public opinion dissemination, and to identify the public opinion accurately have become the hot researches. In this paper, we define the user influence, topic popularity, topic interest to analysis the process of opinions fusion among the users under the interest and confidence threshold. We propose a new public opinion evolution HK–SEIR model which combines the opinion fusion HK and the epidemic transmission SEIR models. Firstly, the topic interest degree is added to the opinion fusion HK model, and the interaction behavior between the users under the interest and confidence threshold is analyzed. Then, we calculate the probability of topic propagation caused by the interaction of opinions between users under group pressure, and the probability that users change from the infected state to the removed state under topic popularity. Finally, we analyze the changes of the susceptible, exposed, infected and removed states in the process of public opinion communication. The experiment proves that the HK–SEIR model is closer to the work-rest rules of public opinion communication than SEIR, SIR model. The density peak time is closer to the peak of real public opinion communication. We find that the user interest is the main factor influencing the public opinion dissemination after the interaction of user opinions fusion reaches a certain degree. The negative public opinion of the higher proportion can easily reach the peak of public opinion propagation.

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