In dynamic Neural Radiance Fields (NeRF) systems, state-of-the-art novel view synthesis methods often fail under significant viewpoint deviations, producing unstable and unrealistic renderings. To address this, we introduce Expanded Dynamic NeRF (ExpanDyNeRF), a monocular NeRF framework that leverages Gaussian splatting priors and a pseudo-ground-truth generation strategy to enable realistic synthesis under large-angle rotations. ExpanDyNeRF optimizes density and color features to improve scene reconstruction from challenging perspectives. We also present the Synthetic Dynamic Multiview (SynDM) dataset, which is the first synthetic multiview dataset for dynamic scenes with explicit side-view supervision, created using a custom GTA V-based rendering pipeline. Quantitative and qualitative results on SynDM and real-world datasets demonstrate that ExpanDyNeRF significantly outperforms existing dynamic NeRF methods in rendering fidelity under extreme viewpoint shifts. Further details are provided in the supplementary materials. Code is available at https://github.com/ostadabbas/ExpanDyNeRF.

Diffusion models have proven effective at generating high-quality images from learned distributions, but their application to the temporal domain, especially for driving scenarios, remains underexplored. Our work addresses key challenges in existing simulations, such as limited data quality, diversity, and high costs, by extending diffusion models to generate realistic long driving videos. We introduce the Dualconditioned Temporal Diffusion Model (DcTDM), an opensource method that incorporates dual conditioning to enforce temporal consistency by guiding frame transitions. Alongside DcTDM, we present DriveSceneDDM, a comprehensive driving video dataset featuring textual scene descriptions, dense depth maps, and canny edge data. We evaluate DcTDM using common video quality metrics, demonstrating its superior performance over other video diffusion models by producing long, temporally consistent driving videos up to 40s, achieving over 25% improvement in consistency and frame quality.

Evaluating autonomous ground vehicles requires evaluating their mobility performance. Since autonomous vehicles are envisioned to make decisions in a variety of situations and environments too diverse to practically assess with only physical testing, their development, and evaluation will necessarily include the use of simulations. These simulations must represent reality sufficiently to represent the decisions that the vehicles would make in real-world. In this paper we present our Scoping Autonomous Vehicle Simulation (SAVeS) platform for benchmarking the performance of simulated environments for autonomous ground vehicle testing1.