Summary of T-readi: Transformer-powered Robust and Efficient Multimodal Inference For Autonomous Driving, by Pengfei Hu et al.
t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving
by Pengfei Hu, Yuhang Qian, Tianyue Zheng, Ang Li, Zhe Chen, Yue Gao, Xiuzhen Cheng, Jun Luo
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); 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 A novel approach to multimodal sensor fusion, titled tREADi, is presented for autonomous vehicles (AVs). Existing methods assume similar data distributions and constant availability, which are rarely true in practice. AVs equipped with camera, lidar, and radar sensors must account for varying resolutions, failures, and losses of modalities. tREADi adapts model parameters to accommodate this variability while maintaining compatibility with existing fusion methods. A cross-modality contrastive learning method is also introduced to compensate for missing modalities. Experimental results show that tREADi improves average inference accuracy by over 6% and reduces latency by almost 15x, with a moderate increase in memory overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine driving a self-driving car that can see the road ahead using cameras, lidar, and radar sensors. Right now, these sensors don’t work well together, which is a problem. A team of researchers created a new way to combine this information called tREADi. It adapts to changing conditions on the road, like different camera resolutions or lost sensor data. This makes the car’s perception more robust and efficient. Tests show that tREADi improves accuracy by 6% and reduces processing time by 15x compared to current methods. |
Keywords
» Artificial intelligence » Inference
