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BYD Automotive Co., Ltd.

Industry researchasia · cn
Official website
Research library5linked papers
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Selected work

Representative Papers

HyWorldVLA: A Vision-Language-Action Model with Hybrid World Modeling for Autonomous Driving

Jul 23, 2026

Existing world models for autonomous driving struggle to balance robustness and interpretability between pixel-level prediction and latent representations. This work proposes a hybrid world modeling framework that unifies pixel supervision and latent representation learning through a two-stage training strategy: in the pretraining stage, it jointly optimizes video latent feature prediction and pixel-level frame reconstruction; in the fine-tuning stage, it relies solely on latent features to drive an action expert. This approach achieves concurrent improvements in fine-grained spatiotemporal reasoning and noise robustness. Evaluated on NAVSIM v1/v2, the method significantly outperforms baselines based exclusively on pixels or latent representations and establishes a new benchmark for evaluating noise robustness in autonomous driving world models.

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TF-Lane: Traffic Flow Module for Robust Lane Perception

Feb 01, 2026

This work addresses the performance degradation of existing lane perception methods under challenging conditions such as occlusion or missing lane markings, where visual cues are insufficient, and highlights the high cost and poor real-time capability of HD map–dependent approaches. To overcome these limitations, the authors propose a Traffic Flow-aware Module (TFM), which, for the first time, leverages real-time, zero-cost traffic flow information as an auxiliary modality for lane perception—requiring neither additional hardware nor HD maps. TFM employs deep learning to extract dynamic traffic flow features and integrates them with mainstream lane detection models through multimodal fusion. Experiments on the NuScenes and OpenLaneV2 datasets demonstrate consistent performance gains across four state-of-the-art models upon incorporating TFM, with mAP improvements of up to 4.1%, significantly enhancing robustness in complex driving scenarios.

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Scene-Adaptive Motion Planning with Explicit Mixture of Experts and Interaction-Oriented Optimization

May 18, 2025

Autonomous trajectory planning in complex urban environments faces key challenges: difficulty in modeling multimodal behavior, poor generalization of single-expert models, and insufficient modeling of vehicle–environment interactions. To address these, this paper proposes an Explicit Mixture-of-Experts (EMoE) dynamic routing framework. Its core contributions are: (1) the first scene-aware explicit MoE architecture, employing a learnable router for task-adaptive expert selection; (2) a multimodal prior query mechanism to enhance diversity-aware trajectory modeling; and (3) an interaction-aware graph neural network coupled with a co-optimization loss function to explicitly capture bidirectional influences between the ego-vehicle and dynamic environmental agents. Evaluated on the NuPlan benchmark, our method achieves state-of-the-art performance across all major test scenarios, significantly improving planning success rate, ride comfort, and safety.

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Recent publications

Latest Papers

HyWorldVLA: A Vision-Language-Action Model with Hybrid World Modeling for Autonomous Driving

Jul 23, 2026

Existing world models for autonomous driving struggle to balance robustness and interpretability between pixel-level prediction and latent representations. This work proposes a hybrid world modeling framework that unifies pixel supervision and latent representation learning through a two-stage training strategy: in the pretraining stage, it jointly optimizes video latent feature prediction and pixel-level frame reconstruction; in the fine-tuning stage, it relies solely on latent features to drive an action expert. This approach achieves concurrent improvements in fine-grained spatiotemporal reasoning and noise robustness. Evaluated on NAVSIM v1/v2, the method significantly outperforms baselines based exclusively on pixels or latent representations and establishes a new benchmark for evaluating noise robustness in autonomous driving world models.

0 citationsRead paper

TF-Lane: Traffic Flow Module for Robust Lane Perception

Feb 01, 2026

This work addresses the performance degradation of existing lane perception methods under challenging conditions such as occlusion or missing lane markings, where visual cues are insufficient, and highlights the high cost and poor real-time capability of HD map–dependent approaches. To overcome these limitations, the authors propose a Traffic Flow-aware Module (TFM), which, for the first time, leverages real-time, zero-cost traffic flow information as an auxiliary modality for lane perception—requiring neither additional hardware nor HD maps. TFM employs deep learning to extract dynamic traffic flow features and integrates them with mainstream lane detection models through multimodal fusion. Experiments on the NuScenes and OpenLaneV2 datasets demonstrate consistent performance gains across four state-of-the-art models upon incorporating TFM, with mAP improvements of up to 4.1%, significantly enhancing robustness in complex driving scenarios.

0 citationsRead paper

Scene-Adaptive Motion Planning with Explicit Mixture of Experts and Interaction-Oriented Optimization

May 18, 2025

Autonomous trajectory planning in complex urban environments faces key challenges: difficulty in modeling multimodal behavior, poor generalization of single-expert models, and insufficient modeling of vehicle–environment interactions. To address these, this paper proposes an Explicit Mixture-of-Experts (EMoE) dynamic routing framework. Its core contributions are: (1) the first scene-aware explicit MoE architecture, employing a learnable router for task-adaptive expert selection; (2) a multimodal prior query mechanism to enhance diversity-aware trajectory modeling; and (3) an interaction-aware graph neural network coupled with a co-optimization loss function to explicitly capture bidirectional influences between the ego-vehicle and dynamic environmental agents. Evaluated on the NuPlan benchmark, our method achieves state-of-the-art performance across all major test scenarios, significantly improving planning success rate, ride comfort, and safety.

0 citationsRead paper