Physics-Aware Hyperspectral-LiDAR Fusion for Generative Modeling of Realistic Driving Environments
Keywords:
hyperspectral imaging, LiDAR fusion, generative modeling, physics-aware simulation, autonomous driving, system architectureAbstract
The generation of high-fidelity, physically consistent driving environments is essential for the scalable development and validation of autonomous vehicle systems. While existing generative models rely predominantly on RGB video and depth data, they often fail to capture the full spectral richness and material-specific properties of real-world scenes. This paper proposes a novel paradigm that fuses hyperspectral imaging with LiDAR point clouds under a physics-aware generative framework. Hyperspectral data provides material composition and spectral reflectance information beyond visible bands, while LiDAR offers precise three-dimensional geometry. Integrating these modalities with physics-informed constraints—such as illumination models, surface bidirectional reflectance distribution functions, and atmospheric scattering—enables the generation of driving scenes that are not only visually realistic but also physically coherent. We examine the architectural implications of such fusion within latent diffusion and transformer-based generative backbones, emphasizing structural trade-offs in sensor alignment, bandwidth allocation, and computational efficiency. System-level considerations are discussed, including deployment on edge computing nodes, data governance for spectral privacy, sustainability of large-scale simulation pipelines, and robustness to adversarial perturbations and domain shifts. Fairness implications arise from spectral bias in material recognition across diverse geographic regions, requiring policy interventions. We also analyze the role of band ordering strategies in fusion networks and the alignment of generated video sequences with underlying physics through 3D representation constraints. This work provides a comprehensive systems perspective on hyperspectral-LiDAR generative modeling, positioning it as a critical infrastructure component for next-generation autonomous driving simulation and validation.
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