Are AI-Generated Driving Videos Ready for Autonomous Driving? A Diagnostic Evaluation Framework
Recent text-to-video models have enabled the generation of high-resolution driving scenes from natural language prompts. These AI-generated driving videos (AIGVs) offer a low-cost, scalable alternative to real or simulator data for autonomous driving (AD). But a key question remains: can such videos reliably support training and evaluation of AD models? We present a diagnostic framework that systematically studies this question. First, we introduce a taxonomy of frequent AIGV failure modes, including visual artifacts, physically implausible motion, and violations of traffic semantics, and demonstrate their negative impact on object detection, tracking, and instance segmentation. To support this analysis, we build ADGV-Bench, a driving-focused benchmark with human quality annotations and dense labels for multiple perception tasks. We then propose ADGVE, a driving-aware evaluator that combines static semantics, temporal cues, lane obedience signals, and Vision-Language Model(VLM)-guided reasoning into a single quality score for each clip. Experiments show that blindly adding raw AIGVs can degrade perception performance, while filtering them with ADGVE consistently improves both general video quality assessment metrics and downstream AD models, and turns AIGVs into a beneficial complement to real-world data. Our study highlights both the risks and the promise of AIGVs, and provides practical tools for safely leveraging large-scale video generation in future AD pipelines.
Code (0)
등록된 구현이 없습니다.
Tasks
Video Quality AssessmentInstance SegmentationAutonomous DrivingObject DetectionSimilar Papers 제목 키워드 기반
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving
The advancement of autonomous driving technologies necessitates increasingly sophisticated methods for understanding and predicting real-world scenarios. Vision language models (VLMs) are emerging as revolutionary tools …
Autonomous DrivingDenoisingIn-Context LearningLanguage Modeling+3DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation
World models have demonstrated superiority in autonomous driving, particularly in the generation of multi-view driving videos. However, significant challenges still exist in generating customized driving videos. In this …
Autonomous DrivingLanguage ModelingLanguage ModellingLarge Language Model+1Panacea: Panoramic and Controllable Video Generation for Autonomous Driving
The field of autonomous driving increasingly demands high-quality annotated training data. In this paper, we propose Panacea, an innovative approach to generate panoramic and controllable videos in driving scenarios, cap…
Autonomous DrivingVideo GenerationCoGen: 3D Consistent Video Generation via Adaptive Conditioning for Autonomous Driving
Recent progress in driving video generation has shown significant potential for enhancing self-driving systems by providing scalable and controllable training data. Although pretrained state-of-the-art generation models,…
3D GenerationAutonomous DrivingVideo GenerationUniMLVG: Unified Framework for Multi-view Long Video Generation with Comprehensive Control Capabilities for Autonomous Driving
The creation of diverse and realistic driving scenarios has become essential to enhance perception and planning capabilities of the autonomous driving system. However, generating long-duration, surround-view consistent d…
Autonomous DrivingDiversityVideo Generation