Generative pre-training via neural rendering has become a cornerstone for scaling 3D perception in autonomous driving. However, prevalent approaches relying on implicit Neural Radiance Fields (NeRFs) face two fundamental limitations: the shape-radiance ambiguity inherent in vision-centric optimization and the prohibitive computational overhead of volumetric ray marching. To address these challenges, we propose AES-Gaussian, a novel multi-modal pre-training framework grounded in the efficient 3D Gaussian Splatting (3DGS) representation. Diverging from symmetric fusion paradigms, our core innovation is an Asymmetric Encoder architecture that couples a deep semantic vision backbone with a lightweight, physics-aware LiDAR branch. In this framework, LiDAR data serve not merely for semantic extraction, but as sparse physical anchors. By employing a novel Explicit Feature Synergy mechanism, we directly inject raw LiDAR intensity and depth priors into the Gaussian decoding process, thereby rigidly constraining scene geometry in open-world environments. Extensive empirical validation on the nuScenes dataset demonstrates the superiority of our approach. AES-Gaussian achieves state-of-the-art transfer performance, yielding a substantial 7.0% improvement in NDS for 3D Object Detection and a 4.8% mIoU gain in 3D semantic occupancy prediction compared to baselines. Notably, our method reduces geometric reconstruction error by over 50% while significantly improving training and inference efficiency, attributed to the streamlined asymmetric design and rapid Gaussian rasterization. Ultimately, by enhancing both perception accuracy and system efficiency, this work contributes to the development of safer and more reliable autonomous driving systems.

Deep learning-based perception algorithms in autonomous driving are vulnerable to adversarial examples. Existing adversarial attacks methods often struggle to reconcile the trade-off between visual fidelity and high adversarial aggressiveness, either generating conspicuous noise patterns that lack visual naturalness or requiring computationally expensive iterative optimization during inference. To address these limitations, we propose DSAdv, a novel framework for high-fidelity adversarial example generation via improved LoRA fine-tuning of Latent Diffusion Models (LDMs). We introduce a decoupled Dual-Stream LoRA architecture consisting of an Adversarial Stream to inject blinding perturbations and an Integrity Keeper Stream to maintain semantic realism. These streams are dynamically fused via an Adaptive Input Gating mechanism, effectively resolving the dynamic coordination problem between visual fidelity and high adversariality. Furthermore, our method guides the LoRA fine-tuning of the pre-trained LDM by backpropagating gradients from a target detector, which significantly shortens the inference process. Extensive experiments on the NuScenes, KITTI, and the Science Island datasets validate that DSAdv generates naturalistic adversarial examples with higher transferability and visual quality compared to state-of-the-art methods like AdvDiff and PGD. The proposed framework enables the efficient generation of naturalistic and stealthy adversarial scenarios, providing a rigorous tool for evaluating and enhancing the robustness of autonomous driving systems.