CARLA-GS: Unified Corner-Case Synthesis

CARLA-GS offers a unified, modular pipeline for synthesizing photorealistic and physically consistent corner cases in autonomous driving simulation.

Jul 9 at 8:02 PM6 min read

Diagram illustrating the CARLA-GS modular pipeline for corner-case synthesis in autonomous driving simulation. — The CARLA-GS framework integrates visual reconstruction, LLM reasoning, and physics-based control for advanced corner-case generation.

Visual TL;DR. Robust AD Safety requires Existing Simulators Fail. Existing Simulators Fail solves CARLA-GS Pipeline. CARLA-GS Pipeline uses Editable Gaussian Scene. Editable Gaussian Scene informs Multi-agent LLM. Multi-agent LLM generates Intent-driven Trajectories. Intent-driven Trajectories guides Low-level Motion Control. CARLA-GS Pipeline enables Photorealistic Corner Cases.

Robust AD Safety: need for simulating rare, safety-critical interactions in autonomous driving
Existing Simulators Fail: diffusion models struggle with spatiotemporal consistency and physical realism
CARLA-GS Pipeline: unified, modular framework for corner-case synthesis
Editable Gaussian Scene: reconstructs real driving data with geometry-consistent constraints
Multi-agent LLM: performs scene-level reasoning, identifies risky interactions
Intent-driven Trajectories: generates high-level waypoint paths for agents
Low-level Motion Control: delegated to specific modules for execution
Photorealistic Corner Cases: synthesizes physically consistent and realistic simulation scenarios

Visual TL;DRQuickExplainDeeper

The quest for robust autonomous driving safety hinges on effectively simulating rare, safety-critical interactions. Existing simulators often tackle corner-case generation in isolation, with diffusion models struggling to maintain spatiotemporal consistency and physical realism. This paper introduces CARLA-GS, a novel pipeline that addresses these limitations by unifying diverse generation components within a single, modular framework.

Bridging Semantic Reasoning and Physical Execution

CARLA-GS tackles the multi-faceted problem of corner-case synthesis by strategically decoupling, yet tightly coupling, its core modules. Starting with real driving data, it reconstructs an editable Gaussian scene, incorporating geometry-consistent constraints. This visual foundation is then leveraged by a multi-agent LLM, which performs scene-level reasoning to pinpoint risky interactions and generate high-level, intent-driven waypoint trajectories. Crucially, the low-level motion control is delegated to CARLA, ensuring kinematic and dynamic feasibility via a PID controller. This architecture allows for semantic understanding and physically executable motion to coexist, enhancing the realism and controllability of simulated scenarios.

Photorealistic Corner Cases with Spatiotemporal Fidelity

The framework's innovation lies in its ability to generate photorealistic, spatiotemporally consistent videos that align with both semantic intent and physically feasible motion. By re-projecting simulated vehicle states back into the Gaussian scene for ego-centric rendering, CARLA-GS achieves a high degree of visual fidelity. Experiments conducted on the Waymo Open Dataset demonstrate the system's capability for controllable corner-case generation, producing outputs that are both visually convincing and behaviorally sound. This advancement is critical for training and validating autonomous driving systems against the most challenging edge cases.

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#AI Research #Autonomous Driving #Simulation #Computer Vision #LLMs