DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing

TL;DR

DiffRoad is proposed, a novel diffusion model designed to produce controllable and high-fidelity 3D road scenarios that can be fully automated into the OpenDRIVE format, facilitating generalized autonomous vehicle simulation testing.

Abstract

Generating realistic and diverse road scenarios is essential for autonomous vehicle testing and validation. Nevertheless, owing to the complexity and variability of real-world road environments, creating authentic and varied scenarios for intelligent driving testing is challenging. In this paper, we propose DiffRoad, a novel diffusion model designed to produce controllable and high-fidelity 3D road scenarios. DiffRoad leverages the generative capabilities of diffusion models to synthesize road layouts from white noise through an inverse denoising process, preserving real-world spatial features. To enhance the quality of generated scenarios, we design the Road-UNet architecture, optimizing the balance between backbone and skip connections for high-realism scenario generation. Furthermore, we introduce a road scenario evaluation module that screens adequate and reasonable scenarios for intelligent driving testing using two critical metrics: road continuity and road reasonableness. Experimental results on multiple real-world datasets demonstrate DiffRoad's ability to generate realistic and smooth road structures while maintaining the original distribution. Additionally, the generated scenarios can be fully automated into the OpenDRIVE format, facilitating generalized autonomous vehicle simulation testing. DiffRoad provides a rich and diverse scenario library for large-scale autonomous vehicle testing and offers valuable insights for future infrastructure designs that are better suited for autonomous vehicles.

Figures (10)

• Figure 1: The architecture of DiffRoad. The overarching framework of DiffRoad primarily comprises the road structure data generation model based on the enhanced conditional diffusion model, alongside the scene-level evaluation and filtering module. DiffRoad incorporates the road attention mechanism and Road MultiFreeU-Net (Road-UNet) module, iteratively refining the noise estimation based on the road attribute information to synthesize realistic and diverse road scenarios.
• Figure 2: Controllable and diverse 3D road scenario generation. DiffRoad enables the generation of specific types of road scenarios based on road attributes and conditional information, thereby meeting the requirements for AV testing.
• Figure 3: Qualitative results of the DiffRoad model's generation process. The DiffRoad model can generate road scenes based on user input specifying the desired type of scene, such as "three three-way intersections." The model begins by sampling noise from a Gaussian distribution at $k$ = 500. Through a gradual denoising process, DiffRoad refines the sampled noise until convergence at $k$ = 0. The comparative analysis with real-world road scenes reveals that DiffRoad effectively captures the intricate characteristics of real-world road scenes, producing realistic and diverse road configurations.
• Figure 4: Controllable and diverse 3D road scenario generation. DiffRoad enables the generation of specific types of road scenarios based on road attributes and conditional information, thereby meeting the requirements for AV testing.
• Figure 5: Visualization of the generated diverse and realistic 3D road scenarios.