Summary of Rag-based Explainable Prediction Of Road Users Behaviors For Automated Driving Using Knowledge Graphs and Large Language Models, by Mohamed Manzour Hussien et al.
RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models
by Mohamed Manzour Hussien, Angie Nataly Melo, Augusto Luis Ballardini, Carlota Salinas Maldonado, Rubén Izquierdo, Miguel Ángel Sotelo
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Neural and Evolutionary Computing (cs.NE)
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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 predicting road users’ behaviors is proposed, moving beyond traditional kinematic-based methods by incorporating contextual semantic information. The method integrates Knowledge Graphs and Large Language Models using Retrieval Augmented Generation techniques, allowing for explainable predictions that rely on legacy graph information and real-time sensor data. Two use cases are demonstrated: pedestrian crossing action prediction and lane change maneuver prediction. The proposed approach outperforms the state of the art in terms of anticipation and F1-score. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re designing a smart car that can predict what people will do next, like whether they’ll cross the road or change lanes. Most current approaches only look at how fast someone is moving, but humans are influenced by their surroundings. This paper proposes a new way to make predictions using special computer models called Knowledge Graphs and Large Language Models. These models work together to understand the context of what’s happening around the car and provide explanations for its predictions. The authors tested this approach on two scenarios: predicting pedestrians’ actions and lane changes. Their method performed better than existing approaches, which could lead to safer and more efficient autonomous vehicles. |
Keywords
» Artificial intelligence » F1 score » Retrieval augmented generation
