Summary of Less Is More: Efficient Brain-inspired Learning For Autonomous Driving Trajectory Prediction, by Haicheng Liao et al.
Less is More: Efficient Brain-Inspired Learning for Autonomous Driving Trajectory Prediction
by Haicheng Liao, Yongkang Li, Zhenning Li, Chengyue Wang, Chunlin Tian, Yuming Huang, Zilin Bian, Kaiqun Zhu, Guofa Li, Ziyuan Pu, Jia Hu, Zhiyong Cui, Chengzhong Xu
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 paper presents the Human-Like Trajectory Prediction model (HLTP++) for autonomous driving, which mimics human cognitive processes to improve trajectory prediction. HLTP++ incorporates a novel teacher-student knowledge distillation framework, where the “teacher” model adapts attention based on spatial orientation, proximity, and speed, while the “student” model focuses on real-time interaction and decision-making. The paper also introduces a Fourier Adaptive Spike Neural Network (FA-SNN) to improve efficiency. Evaluations using NGSIM, HighD, and MoCAD benchmarks demonstrate superior performance, reducing predicted trajectory error by over 11% on NGSIM and 25% on HighD datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making self-driving cars better at predicting where other cars will go. It creates a new model called HLTP++ that thinks like humans do when driving. This helps the car make more accurate predictions, which makes it safer and better at navigating roads. The model uses a special way of learning that’s inspired by how our brains work. The results show that this model is much better than other models already used in self-driving cars. |
Keywords
» Artificial intelligence » Attention » Knowledge distillation » Neural network
