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Wild-Drive: Off-Road Scene Captioning and Path Planning via Robust Multi-modal Routing and Efficient Large Language Model
arXiv – CS AI|Zihang Wang, Xu Li, Benwu Wang, Wenkai Zhu, Xieyuanli Chen, Dong Kong, Kailin Lyu, Yinan Du, Yiming Peng, Haoyang Che||9 views
🤖AI Summary
Researchers introduced Wild-Drive, a framework for autonomous off-road driving that combines scene captioning and path planning using multimodal AI. The system addresses challenges in harsh weather conditions through robust sensor fusion and efficient large language models, outperforming existing methods in degraded sensing conditions.
Key Takeaways
- →Wild-Drive framework enables explainable autonomous driving in off-road environments through natural language scene descriptions.
- →The system uses MoRo-Former bridge to adaptively aggregate reliable sensor data under adverse weather conditions.
- →Integration of efficient LLMs with planning tokens enables simultaneous scene captioning and trajectory prediction.
- →OR-C2P Benchmark provides standardized testing for off-road autonomous driving under various sensor corruption scenarios.
- →Experimental results show improved stability and performance compared to existing LLM-based autonomous driving methods.
#autonomous-driving#llm#multimodal-ai#computer-vision#robotics#sensor-fusion#path-planning#off-road#explainable-ai
Read Original →via arXiv – CS AI
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