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Kun Jiang

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5 papers

2 author rows

EAAI Journal 2026 Journal Article

Elastic net twin support vector machine with Universum data and its safe screening rule

Hongmei Wang
Ping Li
Kun Jiang
Yitian Xu

Twin support vector machine with Universum data (UTSVM) is a powerful classification technique. It not only inherits the sophisticated structure of twin support vector machine but also incorporates prior information from Universum data. However, the adoption of l 1 penalty for slack variables in UTSVM suffers a geometric irrationality, impairing its ability to precisely represent the location of violated samples and thus partially degrading model performance. Therefore, we propose a novel elastic net twin support vector machine with Universum data (ENUTSVM) in this paper, which refines the geometric formulation by imposing elastic net penalty for slack variables. Furthermore, we theoretically derive a violation tolerance upper bound (VTUB) that quantitatively characterizes the relationship between the distances of violated samples and their corresponding slack variable differences. Additionally, to enhance the computational efficiency of ENUTSVM, we develop a safe screening rule (SSR-ENUTSVM) by combining variational inequalities and optimization conditions. We compare the proposed method with seven other competitive algorithms on four synthetic datasets and ten benchmark datasets. The experimental results and statistical tests confirm the superiority of our methods. Finally, we apply our method to an epileptic electroencephalogram (EEG) signal classification problem, which verifies its effectiveness in practical applications.

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TAAS Journal 2026 Journal Article

Federated Meta-Learning for Autonomous System in VEC-Enabled ICVs

Chunlin Li
Kun Jiang
Sihan Zeng
Guangxuan He
Shaohua Wan

Autonomous systems in VEC-enabled ICVs face many challenges, such as self-organization, privacy breach risks, vehicle selection, and resource allocation. As a distributed training framework, Federated Meta-Learning (FML) provides a powerful tool for adaptive and efficient processing of vehicular tasks while securing vehicle data privacy in VEC-enabled ICVs. However, the high-speed mobility of vehicles leads to higher latency and communication interruptions. This article investigates the vehicle selection and resource allocation scheme, subject to the constraints on the number and the residence time of vehicles, the maximum transmission energy consumption, and the ratio of bandwidth resource allocation. It is proved to be a challenging mixed-integer nonlinear programming problem, and we formulate it as a Markov decision process (MDP). We proposed an adaptive Sum Tree-Deep Recurrent Q-network algorithm (ST-DRQN) to solve the optimal resource allocation. ST-DRQN employs an enhanced empirical selection rule and a proportional priority sampling method to address the problems of inefficient model training and slow convergence. Finally, we conducted experiments using intelligent cars equipped with Raspberry Pi to show the effectiveness of the proposed methodology. Experimental results demonstrate that ST-DRQN achieves adaptability and credibility among ICVs while reducing latency and energy costs incurred by long-term training of FML.

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NeurIPS Conference 2025 Conference Paper

COME: Adding Scene-Centric Forecasting Control to Occupancy World Model

Yining Shi
Kun Jiang
Qiang Meng
Ke Wang
Jiabao Wang
Wenchao Sun
Tuopu Wen
MengMeng Yang

World models are critical for autonomous driving to simulate environmental dynamics and generate synthetic data. Existing methods struggle to disentangle ego-vehicle motion (perspective shifts) from scene evolvement (agent interactions), leading to suboptimal predictions. Instead, we propose to separate environmental changes from ego-motion by leveraging the scene-centric coordinate systems. In this paper, we introduce COME: a framework that integrates scene-centric forecasting Control into the Occupancy world ModEl. Specifically, COME first generates ego-irrelevant, spatially consistent future features through a scene-centric prediction branch, which are then converted into scene condition using a tailored ControlNet. These condition features are subsequently injected into the occupancy world model, enabling more accurate and controllable future occupancy predictions. Experimental results on the nuScenes-Occ3D dataset show that COME achieves consistent and significant improvements over state-of-the-art (SOTA) methods across diverse configurations, including different input sources (ground-truth, camera-based, fusion-based occupancy) and prediction horizons (3s and 8s). For example, under the same settings, COME achieves 26. 3% better mIoU metric than DOME and 23. 7% better mIoU metric than UniScene. These results highlight the efficacy of disentangled representation learning in enhancing spatio-temporal prediction fidelity for world models. Code is available at https: //github. com/synsin0/COME.

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ICLR Conference 2024 Conference Paper

LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving

Tianyu Li 0004
Peijin Jia
Bangjun Wang
Li Chen 0008
Kun Jiang
Junchi Yan
Hongyang Li 0001

A map, as crucial information for downstream applications of an autonomous driving system, is usually represented in lanelines or centerlines. However, existing literature on map learning primarily focuses on either detecting geometry-based lanelines or perceiving topology relationships of centerlines. Both of these methods ignore the intrinsic relationship of lanelines and centerlines, that lanelines bind centerlines. While simply predicting both types of lane in one model is mutually excluded in learning objective, we advocate lane segment as a new representation that seamlessly incorporates both geometry and topology information. Thus, we introduce LaneSegNet, the first end-to-end mapping network generating lane segments to obtain a complete representation of the road structure. Our algorithm features two key modifications. One is a lane attention module to capture pivotal region details within the long-range feature space. Another is an identical initialization strategy for reference points, which enhances the learning of positional priors for lane attention. On the OpenLane-V2 dataset, LaneSegNet outperforms previous counterparts by a substantial gain across three tasks, i.e., map element detection (+4.8 mAP), centerline perception (+6.9 DET$_l$), and the newly defined one, lane segment perception (+5.6 mAP). Furthermore, it obtains a real-time inference speed of 14.7 FPS. Code is accessible at https://github.com/OpenDriveLab/LaneSegNet.

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IROS Conference 2023 Conference Paper

SELVO: A Semantic-Enhanced Lidar-Visual Odometry

Kun Jiang
Shuang Gao
Xudong Zhang
Jijunnan Li
Yandong Guo
Shijie Liu
Chunlai Li
Jianyu Wang

In the face of complex external environment, single sensor information can no longer meet the accuracy requirements of low-drift SLAM. In this paper, we focus on the fusion scheme of cameras and lidar, and explore the gain of semantic information to SLAM system. A Semantic-Enhanced Lidar-Visual Odometry (SELVO) is proposed to achieve pose estimation with high accuracy and robustness by applying semantics and utilizing strategies of initialization and sensor fusion. In loop closure detection thread, we propose a novel place recognition method based on semantic information to maintain the global consistency of the map. In the back-end, we design a joint optimization framework including visual odometry, lidar odometry and loop closure detection, and innovatively propose to recognize degraded scenes with semantic information. We have conducted a large number of experiments on KITTI [1] and KITTI-360 [2] dataset, and the results show that our system can achieve the high accuracy and competitive performance in comparison with state-of-the-art methods.

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