<ul data-eligibleForWebStory="false"><li>Researchers have proposed VLAD, a vision-language autonomous driving model that integrates a fine-tuned Visual Language Model (VLM) with VAD, a state-of-the-art end-to-end system.</li><li>VLAD utilizes a specialized fine-tuning approach using custom question-answer datasets to enhance spatial reasoning capabilities.</li><li>The system generates high-level navigational commands for vehicle operation and provides interpretable natural language explanations of driving decisions to increase transparency and trustworthiness.</li><li>Evaluation on the nuScenes dataset shows that VLAD reduces average collision rates by 31.82% compared to baseline methodologies, setting a new benchmark for VLM-augmented autonomous driving systems.</li></ul>

VLAD: A VLM-Augmented Autonomous Driving Framework with Hierarchical Planning and Interpretable Decision Process

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