Developing robust perception systems for autonomous vehicles demands a vast supply of diverse and realistic training data. Yet, gathering real-world driving data is often costly and time- consuming. This proposes a groundbreaking solution: employing Conditional Generative Adversarial Networks (CGANs) implemented in PyTorch to synthesize photorealistic autonomous car scenario images with unmatched control and detail. Imagine a CGAN specifically crafted for automotive environments. Imagine it trained on rich datasets like Cityscapes and CamVid, capturing the intricacies of urban streetscapes, varied weather conditions, and a plethora of traffic participants. Now, integrate a Natural Language Processing (NLP) model into this CGAN, empowering users to dictate the scene through natural language prompts. Specify desired objects (say, a pedestrian crossing the street), their attributes (size, color), and even their spatial relationships (walking behind a parked car). This transcends mere image generation. We rigorously evaluate the model's ability to produce not just photorealistic visuals but also diverse scenarios that faithfully reflect the training data distribution. More importantly, we showcase its potential for crafting challenging and edge-case scenarios – think sudden downpours, construction zones, or even a dog chasing a skateboarder. By equipping autonomous vehicle perception systems to handle such diverse and often unpredictable situations, we pave the way for enhanced safety, reliability, and ultimately, a smoother ride towards the future of autonomous driving.
Integrating Natural Language Processing with CGANs to Generate Customized, Realistic Traffic Scenarios for Autonomous Vehicle Training
2024-07-19
476161 byte
Conference paper
Electronic Resource
English
Ride comfort improvement in different traffic scenarios for autonomous vehicle
European Patent Office | 2025
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