Summary of Perception Without Vision For Trajectory Prediction: Ego Vehicle Dynamics As Scene Representation For Efficient Active Learning in Autonomous Driving, by Ross Greer et al.
Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving
by Ross Greer, Mohan Trivedi
First submitted to arxiv on: 15 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 study proposes methods for clustering trajectory-states and sampling strategies in an active learning framework to reduce annotation and data costs while maintaining model performance. The approach leverages trajectory information to guide data selection, promoting diversity in the training data. The paper demonstrates the effectiveness of the methods on the trajectory prediction task using the nuScenes dataset, showing consistent performance gains over random sampling across different data pool sizes. The results suggest that integrating trajectory-state-informed active learning can lead to more efficient and robust autonomous driving systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study tries to make self-driving cars better by using information about where things are going to help choose which data to use for training. They came up with ways to group together similar routes and pick the most important parts of those routes. This helps the car learn faster and more accurately without needing as much data or training. The researchers tested their ideas on a big dataset and found that they worked really well, especially when there wasn’t as much data available. |
Keywords
» Artificial intelligence » Active learning » Clustering
