Generative AI Pipelines for Safety Validation of Autonomous Driving Models
DOI:
https://doi.org/10.15662/IJARCST.2021.0405003Keywords:
Autonomous Driving, Safety Validation, Generative AI Pipeline, Scenario Generation, Edge Case Simulation, Conditional GANs (cGAN), Simulation-Based TestingAbstract
Ensuring the safety of autonomous driving (AD) systems demands rigorous validation under diverse and rare edge-case scenarios. Traditional validation methods—such as collecting large-scale naturalistic driving data or manually designing challenging situations—are limited by cost, scalability, and unpredictability. We propose Generative AI Pipelines (GAIP) for systematic safety validation of AD models, combining procedural scenario creation, generative modeling, and simulation-based evaluation to generate comprehensive, high-fidelity driving scenarios at scale.
Our pipeline consists of three main stages: (1) Scenario Template Design, where safety engineers define scenario families with parameters (e.g., pedestrian crossing speed, occlusion, weather); (2) Generative Augmentation, where conditional generative adversarial networks (cGANs) and variational autoencoders (VAEs) synthesize realistic visual, behavioral, and sensor data variants of these templates; (3) Validation Simulation, where synthetic scenarios are replayed through AV perception-planning-control stacks in simulation platforms (e.g., CARLA), and safety metrics—such as collision rate, braking adequacy, and time‑to‑collision—are recorded.
Through experiments spanning urban intersections, occluded pedestrian events, and adverse weather conditions, GAIP yields a 25% increase in failure detection compared to manually scripted edge-case testing. Visual realism of synthetic scenes was rated at 4.3/5 by domain experts (compared to 4.7/5 for real scenes), indicating sufficient fidelity for safety evaluation. The pipeline reduces validation time by up to 60% and significantly broadens scenario coverage through parameter-driven sampling.
In summary, our Generative AI Pipelines facilitate scalable, reproducible, and realistic generation of critical safety scenarios, enhancing the assessment of autonomous driving systems. This approach lowers cost, accelerates validation cycles, and uncovers corner-case vulnerabilities, advancing the robustness and safety of tomorrow’s AVs.
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