Summary of M2da: Multi-modal Fusion Transformer Incorporating Driver Attention For Autonomous Driving, by Dongyang Xu et al.
M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving
by Dongyang Xu, Haokun Li, Qingfan Wang, Ziying Song, Lei Chen, Hanming Deng
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Multi-Modal fusion transformer incorporating Driver Attention (M2DA) aims to improve autonomous driving by efficiently integrating multi-modal sensor data and enabling human-like scene understanding. The M2DA model combines a novel Lidar-Vision-Attention-based Fusion (LVAFusion) module with driver attention, allowing for precise identification of critical areas in complex scenarios. Experimental results on the CARLA simulator demonstrate state-of-the-art performance using less data, outperforming closed-loop benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine autonomous cars that can see and understand the world like humans do! This paper helps make that possible by developing a special type of computer model called M2DA. It takes in different types of sensor information from cameras and sensors on the car, and uses this info to “see” the road and its surroundings in a way that’s similar to how a human driver would. The goal is to make self-driving cars safer and more efficient. The researchers tested their model using a simulator and found it performed really well, even with less data than usual. |
Keywords
» Artificial intelligence » Attention » Multi modal » Scene understanding » Transformer
