Author name cluster

Xianpeng Lang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

2 author rows

AAAI Conference 2026 Conference Paper

CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving

Enhui Ma
Lijun Zhou
Tao Tang
Jiahuan Zhang
Junpeng Jiang
Zhan Zhang
Dong Han
Kun Zhan

End-to-end planning methods are the de-facto standard of the current autonomous driving system, while the robustness of the data-driven approaches suffers due to the notorious long-tail problem (i.e., rare but safety-critical failure cases). In this work, we explore whether recent diffusion-based video generation methods (a.k.a. world models), paired with structured 3D layouts, can enable a fully automated pipeline to self-correct such failure cases. We first introduce an agent to simulate the role of product manager, dubbed PM-Agent, which formulates data requirements to collect data similar to the failure cases. Then, we use a generative model that can simulate both data collection and annotation. However, existing generative models struggle to generate high-fidelity data conditioned on 3D layouts. To address this, we propose DriveSora, which can generate spatiotemporally consistent videos aligned with the 3D annotations requested by PM-Agent. We integrate these components into our self-correcting agentic system, CorrectAD. Importantly, our pipeline is end-to-end model agnostic and can be applied to improve any end-to-end planner. Evaluated on both nuScenes and a more challenging in-house dataset across multiple end-to-end planners, CorrectAD corrects 62.5% and 49.8% of failure cases, reducing collision rates by 39% and 27%, respectively.

PDF Details DOI

AAAI Conference 2026 Conference Paper

DriveLiDAR4D: Sequential and Controllable LiDAR Scene Generation for Autonomous Driving

Kaiwen Cai
Xinze Liu
Xia Zhou
Hengtong Hu
Jie Xiang
Luyao Zhang
Xueyang Zhang
Kun Zhan

The generation of realistic LiDAR point clouds plays a crucial role in the development and evaluation of autonomous driving systems. Although recent methods for 3D LiDAR point cloud generation have shown significant improvements, they still face notable limitations, including the lack of sequential generation capabilities and the inability to produce accurately positioned foreground objects and realistic backgrounds. These shortcomings hinder their practical applicability. In this paper, we introduce DriveLiDAR4D, a novel LiDAR generation pipeline consisting of multimodal conditions and a novel sequential noise prediction model LiDAR4DNet, capable of producing temporally consistent LiDAR scenes with highly controllable foreground objects and realistic backgrounds. To the best of our knowledge, this is the first work to address the sequential generation of LiDAR scenes with full scene manipulation capability in an end-to-end manner. We evaluated DriveLiDAR4D on the nuScenes and KITTI datasets, where we achieved an FRD score of 743.13 and an FVD score of 16.96 on the nuScenes dataset, surpassing the current state-of-the-art (SOTA) method, UniScene, with an performance boost of 37.2% in FRD and 24.1% in FVD, respectively.

PDF Details DOI

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.

PDF Details DOI

AAAI Conference 2026 Conference Paper

WorldRFT: Latent World Model Planning with Reinforcement Fine-Tuning for Autonomous Driving

Pengxuan Yang
Ben Lu
Zhongpu Xia
Chao Han
Yinfeng Gao
Teng Zhang
Kun Zhan
Xianpeng Lang

Latent World Models enhance scene representation through temporal self-supervised learning, presenting a perception annotation-free paradigm for end-to-end autonomous driving. However, the reconstruction-oriented representation learning tangles perception with planning tasks, leading to suboptimal optimization for planning. To address this challenge, we propose WorldRFT, a planning-oriented latent world model framework that aligns scene representation learning with planning via a hierarchical planning decomposition and local-aware interactive refinement mechanism, augmented by reinforcement learning fine-tuning (RFT) to enhance safety-critical policy performance. Specifically, WorldRFT integrates a vision-geometry foundation model to improve 3D spatial awareness, employs hierarchical planning task decomposition to guide representation optimization, and utilizes local-aware iterative refinement to derive a planning-oriented driving policy. Furthermore, we introduce Group Relative Policy Optimization (GRPO), which applies trajectory Gaussianization and collision-aware rewards to fine-tune the driving policy, yielding systematic improvements in safety. WorldRFT achieves state-of-the-art (SOTA) performance on both open-loop nuScenes and closed-loop NavSim benchmarks. On nuScenes, it reduces collision rates by 83% (0.30% → 0.05%). On NavSim, using camera-only sensors input, it attains competitive performance with the LiDAR-based SOTA method DiffusionDrive (87.8 vs. 88.1 PDMS).

PDF Details DOI

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.

PDF Details DOI

ICRA Conference 2025 Conference Paper

Generalizing Motion Planners with Mixture of Experts for Autonomous Driving

Qiao Sun 0001
Huimin Wang
Jiahao Zhan
Fan Nie
Xin Wen
Leimeng Xu
Kun Zhan
Peng Jia 0007

Large real-world driving datasets have sparked significant research into various aspects of learning-based motion planners for autonomous driving. These include data augmentation, model architecture, reward design, training strategies, and planner pipelines. In this paper, we review and benchmark previous methods. Experiments show that many of these approaches have limited generalization abilities in planning performance due to overly complex designs or training paradigms. Experiments further reveal that as models are appropriately scaled, many designs become redundant. Therefore, we introduce StateTransformer-2 (STR2), a scalable, decoder-only motion planner. STR2uses a Vision Transformer (ViT) encoder and a mix-of-experts (MoE) causal transformer architecture. The MoE backbone addresses modality collapse and reward balancing by expert routing during training. Extensive experiments on the NuPlan dataset show that our method generalizes better than previous approaches across different test sets and closed-loop simulations. We evaluate its scalability on billions of real-world urban driving scenarios, demonstrating consistent accuracy improvements as both data and model size grow.

Details

AAAI Conference 2025 Conference Paper

OLiDM: Object-aware LiDAR Diffusion Models for Autonomous Driving

Tianyi Yan
Junbo Yin
Xianpeng Lang
Ruigang Yang
Cheng-Zhong Xu
Jianbing Shen

To enhance autonomous driving, innovative approaches have been proposed to generate simulated LiDAR data. However, these methods often face challenges in producing high-quality and controllable foreground objects. To cater to the needs of object-aware tasks in 3D perception, we introduce OLiDM, a novel framework capable of generating controllable and high-fidelity LiDAR data at both the object and scene levels. OLiDM consists of two pivotal components: the Object-Scene Progressive Generation (OPG) module and the Object Semantic Alignment (OSA) module. OPG adapts to user-specific prompts to generate desired foreground objects, which are subsequently employed as conditions in scene generation, ensuring controllable and diverse output at both the object and scene levels. This also facilitates the association of user-defined object-level annotations with the generated LiDAR scenes. Moreover, OSA aims to rectify the misalignment between foreground objects and background scenes, enhancing the overall quality of the generated objects. The broad efficacy of OLiDM is demonstrated across both unconditional and conditional LiDAR generation tasks, as well as 3D perception tasks. Specifically, on the KITTI-360 dataset, OLiDM surpasses prior state-of-the-art methods such as UltraLiDAR by 11.8 in FPD, producing data that closely mirrors real-world distributions. Additionally, in sparse-to-dense LiDAR completion, OLiDM achieves a significant improvement over LiDARGen, with a 57.47% increase in semantic IoU. Moreover, in 3D object detection, OLiDM enhances the performance of mainstream detectors by 2.4% in mAP and 1.9% in NDS, underscoring its potential in advancing 3D perception models.

PDF Details DOI

ICML Conference 2025 Conference Paper

S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking

Tao Tang
Lijun Zhou
Pengkun Hao
Zihang He
Kalok Ho
Shuo Gu
Zhihui Hao
Haiyang Sun

3D multiple object tracking (MOT) plays a crucial role in autonomous driving perception. Recent end-to-end query-based trackers simultaneously detect and track objects, which have shown promising potential for the 3D MOT task. However, existing methods are still in the early stages of development and lack systematic improvements, failing to track objects in certain complex scenarios, like occlusions and the small size of target object’s situations. In this paper, we first summarize the current end-to-end 3D MOT framework by decomposing it into three constituent parts: query initialization, query propagation, and query matching. Then we propose corresponding improvements, which lead to a strong yet simple tracker: S2-Track. Specifically, for query initialization, we present 2D-Prompted Query Initialization, which leverages predicted 2D object and depth information to prompt an initial estimate of the object’s 3D location. For query propagation, we introduce an Uncertainty-aware Probabilistic Decoder to capture the uncertainty of complex environment in object prediction with probabilistic attention. For query matching, we propose a Hierarchical Query Denoising strategy to enhance training robustness and convergence. As a result, our S2-Track achieves state-of-the-art performance on nuScenes benchmark, i. e. , 66. 3% AMOTA on test split, surpassing the previous best end-to-end solution by a significant margin of 8. 9% AMOTA. We achieve 1st place on the nuScenes tracking task leaderboard.

Details

IROS Conference 2024 Conference Paper

CSR: A Lightweight Crowdsourced Road Structure Reconstruction System for Autonomous Driving

Huayou Wang
Qingyao Liu
Jiazheng Wu
Kun Liu
Chao Ding
Xianpeng Lang
Changliang Xue

Highly accurate and robust vectorized reconstruction of road structures is crucial for autonomous vehicles. Traditional LiDAR-based methods require multiple processes and are often expensive, time-consuming, labor-intensive, and cumbersome. In this paper, we propose a lightweight crowdsourced road structure reconstruction system (termed CSR) that relies solely on online perceived semantic elements. Ambiguities and perceptual errors of semantic features and Global Navigation Satellite System (GNSS) global pose errors constitute the predominant challenge in achieving alignment across multi-trip data. To this end, a robust two-phased coarse-to-fine multi-trip alignment method is performed considering local geometric consistency, global topology consistency, intra-trip temporal consistency, and inter-trip consistency. Further, we introduce an incremental pose graph optimization framework with adaptive weight tuning ability to integrate pre-built road structures, currently perceived multi-trip semantic features, odometry, and GNSS, enabling accurate and robust incremental road structure reconstruction. CSR is highly automated, efficient, and scalable for large-scale autonomous driving scenarios, significantly expediting road structure production. We quantitatively and qualitatively validate the reconstruction performance of CSR in real-world scenes. CSR achieves centimeter-level accuracy commensurate with established LiDAR-based methods, concurrently boosting efficiency and reducing resource expenditure.

Details

IROS Conference 2024 Conference Paper

RCAL: A Lightweight Road Cognition and Automated Labeling System for Autonomous Driving Scenarios

Jiancheng Chen
Chao Yu
Huayou Wang
Kun Liu
Yifei Zhan
Xianpeng Lang
Changliang Xue

Vectorized reconstruction and topological cognition of road structures are crucial for autonomous vehicles to handle complex scenes. Traditional frameworks rely heavily on high-definition (HD) maps, which place significant demands on storage, computation, and manual labor. To overcome these limitations, we introduce a lightweight Road Cognition and Automated Labeling (RCAL) system. It leverages lightweight road data captured from mass-produced vehicles to vectorize road elements and cognize their topology. RCAL compiles multi-trip data on cloud servers for enhanced accuracy and coverage, addressing the limitations of single-trip data. In the field of element extraction, we proposed a pivotal point priority sampling strategy that can balance the contradiction between road scale and processing efficiency. Additionally, traffic flow is utilized to enhance the accuracy of road topology cognition. With its impressive automation, reliability, and efficiency, RCAL stands as an advanced solution in the field. Our evaluations on the intersection dataset from the real world confirm that RCAL not only achieves comparable precision to traditional HD map labeling systems but also substantially reducing resource costs.

Details