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DriveEnv-NeRF: Exploration of A NeRF-Based Autonomous Driving Environment for Real-World Performance Validation

Mu-Yi Shen, Chia-Chi Hsu, Hao-Yu Hou, Yu-Chen Huang, Wei-Fang Sun, Chia-Che Chang, Yu-Lun Liu, Chun-Yi Lee

TL;DR

DriveEnv-NeRF addresses the critical sim-to-real gap in autonomous driving by constructing a target real scene as a NeRF-based simulator that renders novel views and provides 3D meshes for collision emulation, integrated with a physics engine and calibrated hardware settings. The framework uses appearance embeddings to vary lighting, enabling robust DRL policy training and validation across diverse conditions while forecasting real-world performance. Through two navigation tasks and real-world testing on a UGV, the approach demonstrates that NeRF-based validation environments can predict relative policy performance and improve transfer robustness, albeit with residual calibration and dynamics gaps. This method offers a cost-effective, scalable pathway for real-scene validation and policy development, potentially reducing the need for extensive real-world trials during training and evaluation.

Abstract

In this study, we introduce the DriveEnv-NeRF framework, which leverages Neural Radiance Fields (NeRF) to enable the validation and faithful forecasting of the efficacy of autonomous driving agents in a targeted real-world scene. Standard simulator-based rendering often fails to accurately reflect real-world performance due to the sim-to-real gap, which represents the disparity between virtual simulations and real-world conditions. To mitigate this gap, we propose a workflow for building a high-fidelity simulation environment of the targeted real-world scene using NeRF. This approach is capable of rendering realistic images from novel viewpoints and constructing 3D meshes for emulating collisions. The validation of these capabilities through the comparison of success rates in both simulated and real environments demonstrates the benefits of using DriveEnv-NeRF as a real-world performance indicator. Furthermore, the DriveEnv-NeRF framework can serve as a training environment for autonomous driving agents under various lighting conditions. This approach enhances the robustness of the agents and reduces performance degradation when deployed to the target real scene, compared to agents fully trained using the standard simulator rendering pipeline.

DriveEnv-NeRF: Exploration of A NeRF-Based Autonomous Driving Environment for Real-World Performance Validation

TL;DR

DriveEnv-NeRF addresses the critical sim-to-real gap in autonomous driving by constructing a target real scene as a NeRF-based simulator that renders novel views and provides 3D meshes for collision emulation, integrated with a physics engine and calibrated hardware settings. The framework uses appearance embeddings to vary lighting, enabling robust DRL policy training and validation across diverse conditions while forecasting real-world performance. Through two navigation tasks and real-world testing on a UGV, the approach demonstrates that NeRF-based validation environments can predict relative policy performance and improve transfer robustness, albeit with residual calibration and dynamics gaps. This method offers a cost-effective, scalable pathway for real-scene validation and policy development, potentially reducing the need for extensive real-world trials during training and evaluation.

Abstract

In this study, we introduce the DriveEnv-NeRF framework, which leverages Neural Radiance Fields (NeRF) to enable the validation and faithful forecasting of the efficacy of autonomous driving agents in a targeted real-world scene. Standard simulator-based rendering often fails to accurately reflect real-world performance due to the sim-to-real gap, which represents the disparity between virtual simulations and real-world conditions. To mitigate this gap, we propose a workflow for building a high-fidelity simulation environment of the targeted real-world scene using NeRF. This approach is capable of rendering realistic images from novel viewpoints and constructing 3D meshes for emulating collisions. The validation of these capabilities through the comparison of success rates in both simulated and real environments demonstrates the benefits of using DriveEnv-NeRF as a real-world performance indicator. Furthermore, the DriveEnv-NeRF framework can serve as a training environment for autonomous driving agents under various lighting conditions. This approach enhances the robustness of the agents and reduces performance degradation when deployed to the target real scene, compared to agents fully trained using the standard simulator rendering pipeline.
Paper Structure (13 sections, 5 figures, 1 table)

This paper contains 13 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology: DriveEnv-NeRF Framework
  4. Construction of the Simulation Environment
  5. Validation and Training of DRL Agents
  6. Experimental Results
  7. Experimental Setup
  8. Quantitative Results
  9. Sim-to-Real Gap Analysis
  10. Discussion and Conclusion
  11. An Extended Review of the Related Work
  12. Details of the Experimental Setups
  13. Extended Discussion and Conclusion

Figures (5)

  • Figure 1: Visualization of the scenes rendered by DriveEnv-NeRF for emulating diverse lighting conditions using different setups of the appearance embedding for training policies.
  • Figure 2: An overview of the DriveEnv-NeRF framework.
  • Figure 3: The application of DriveEnv-NeRF shows agent's adaptability to diverse lighting.
  • Figure 4: Examples of (a) an image rendered by the simulator, (b) a NeRF rendered image, and (c) a real image.
  • Figure 5: DriveEnv-NeRF enables the interpolation of lighting conditions between two appearance embeddings, which allows generation of a spectrum from a daylight view to a nighttime view. This rendering capability, achieved through the use of appearance embedding, enriches the training and validation process for DRL policies by offering diverse and realistic scenes.