Neural Prior-Based Reconstruction for Robust Autonomous Navigation Against Various Disturbances
Autonomous driving technology has made significant progress in recent years, yet practical deployment remains challenging due to environmental disturbances such as adverse weather conditions, sensor failures, and external interferences. Existing approaches often struggle to maintain stable performance when faced with these challenges. This paper introduces a novel neural prior-based reconstruction framework designed to enhance the robustness of autonomous navigation systems by leveraging implicit scene representations and attention-based feature fusion.
Introduction
Autonomous vehicles rely heavily on perception systems to navigate urban environments and understand their surroundings. While extensive research has focused on driving in favorable conditions, real-world deployment is often hindered by unpredictable disturbances. Traditional methods attempt to address these issues through data augmentation, multi-sensor fusion, or image restoration techniques. However, these approaches either lack realism, introduce high costs, or fail to handle complex multi-view disturbances effectively. Inspired by human drivers who rely on prior experience in familiar environments, this work proposes a neural prior-based framework that densely encodes scene geometry and texture information to improve robustness against disturbances.
Adaptive Neural Radiance Field Construction for Large-Scale Autonomous Driving Scenes
Neural Radiance Fields (NeRF) have emerged as a powerful tool for implicit scene representation, offering photorealistic rendering capabilities. However, traditional NeRF methods are limited to small-scale, static environments and cannot handle the dynamic, unbounded nature of autonomous driving scenarios. To address these limitations, this work introduces an adaptive NeRF construction method that automatically partitions large-scale driving scenes into manageable sub-fields.
The proposed method processes vehicle trajectory data to determine optimal segmentation boundaries, creating multiple sub-NeRFs that collectively cover the entire driving environment. Each sub-NeRF is trained independently while sharing a unified sky background model to maintain consistency across different lighting conditions. This approach eliminates the need for manual scene partitioning, significantly reducing human intervention.
Key innovations include an adaptive bounding box expansion strategy that dynamically adjusts sub-NeRF boundaries based on vehicle movement and an inverse cube projection technique that handles unbounded outdoor scenes. The system also incorporates illumination encoding to account for varying lighting conditions, ensuring stable performance across different times of day and weather scenarios.
Neural Prior-Based Information Reconstruction
With the scene prior encoded in the NeRF structure, the next challenge is effectively utilizing this information to reconstruct perception data under disturbances. The proposed reconstruction framework consists of three main components: prior extraction, multi-view feature encoding, and attention-based fusion.
Prior Extraction
The implicit NeRF representation is converted into a structured voxel grid containing occupancy and feature information. A ray-marching algorithm identifies key points along viewing rays, aggregating spatial features from the NeRF’s hash grid. These features are then downsampled into a compact voxel representation suitable for real-time processing.
Multi-View Feature Encoding
A Variational Autoencoder (VAE) is employed to extract latent features from multi-camera inputs (left-front, front, and right-front views). The VAE learns a compressed representation of clean driving scenes, capturing essential spatial and semantic information. When disturbances occur, the encoder generates corrupted feature representations that will later be corrected using scene priors.
Attention-Based Feature Fusion
The core innovation lies in the attention mechanism that dynamically blends current observations with historical priors. Unlike traditional methods that treat all input data equally, the attention layers implicitly identify and suppress corrupted features while enhancing reliable information. The fusion process occurs through multiple self-attention layers that progressively refine the output, followed by a final MLP that generates the reconstructed features.
This approach demonstrates particular effectiveness in handling complete sensor failures, where information from unaffected camera views can be used to infer missing perspectives. The attention mechanism naturally learns cross-view relationships, enabling robust reconstruction even when multiple sensors are compromised.
Robustness Enhancement Framework for Autonomous Navigation
The reconstructed perception data serves as input to existing autonomous navigation models, significantly improving their performance under disturbances. The framework operates as a preprocessing module that can be integrated with various end-to-end driving models without requiring architectural changes.
Experiments demonstrate that the system maintains high efficiency despite its sophisticated components. The NeRF prior is processed offline, while the online reconstruction pipeline leverages lightweight networks suitable for real-time operation. The attention mechanism’s computational complexity remains manageable by processing only the current frame’s features rather than maintaining long temporal sequences.
Experimental Validation
Comprehensive evaluations were conducted using the CARLA simulator, a high-fidelity autonomous driving platform. Tests focused on three key aspects: scene reconstruction quality, information recovery under disturbances, and navigation robustness improvement.
Scene Reconstruction Performance
The adaptive NeRF construction method achieved superior results compared to baseline techniques like original NeRF and Mip-NeRF 360. Quantitative metrics showed a Peak Signal-to-Noise Ratio (PSNR) of 25.15 and Structural Similarity Index (SSIM) of 0.7932, significantly outperforming alternative approaches. Visual comparisons revealed clearer details and better handling of lighting variations, particularly in large outdoor environments.
Information Reconstruction Under Disturbances
The system was tested against four common disturbance types: sensor occlusions (simulating obstructions like leaves or dirt), noise attacks (emulating electromagnetic interference), brightness interference (representing challenging lighting conditions), and complete sensor failures. Compared to standard VAE reconstruction, the neural prior-enhanced method demonstrated remarkable recovery capabilities:
• For partial occlusions, the attention mechanism successfully filled in missing regions using scene priors
• Noise patterns were effectively suppressed while preserving legitimate image features
• Extreme brightness variations were normalized using illumination-invariant priors
• Complete sensor failures were addressed by synthesizing plausible views from other cameras
Navigation Robustness Improvement
Three state-of-the-art navigation models (Cilrs, LateFusion, and NEAT) were evaluated with and without the proposed framework. Under disturbances, unprotected models suffered severe performance degradation – NEAT’s Driving Score (DS) dropped from 62.52 to just 7.39. With neural prior enhancement, the same model maintained a DS of 51.52, demonstrating an 8.5x improvement in robustness.
The framework proved particularly effective for vision-only models, narrowing the performance gap between camera-based and multi-modal systems. This suggests that proper prior utilization can reduce reliance on expensive sensor suites while maintaining safety.
Conclusion
This work presents a comprehensive solution for robust autonomous navigation through neural prior-based information reconstruction. The key innovation lies in combining adaptive large-scale scene modeling with intelligent attention-based fusion, enabling vehicles to “remember” environment characteristics and compensate for perception disturbances.
The system’s modular design allows seamless integration with existing navigation stacks, while its efficient implementation ensures real-time operation. Future directions include extending the prior representation to incorporate dynamic elements and investigating self-supervised adaptation mechanisms for unfamiliar environments.
By bridging the gap between ideal laboratory conditions and real-world challenges, this framework represents a significant step toward deployable autonomous driving systems capable of handling the complexities of actual road environments.
doi.org/10.19734/j.issn.1001-3695.2024.06.0185
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