Summary of On the Road to Clarity: Exploring Explainable Ai For World Models in a Driver Assistance System, by Mohamed Roshdi et al.
On the Road to Clarity: Exploring Explainable AI for World Models in a Driver Assistance System
by Mohamed Roshdi, Julian Petzold, Mostafa Wahby, Hussein Ebrahim, Mladen Berekovic, Heiko Hamann
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA)
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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 This paper tackles the crucial issue of transparency and safety in Autonomous Driving (AD) systems, where neural networks are typically considered black boxes. To address this, the authors leverage explainable AI (XAI) techniques like feature relevance estimation and dimensionality reduction to create a transparent backbone model for convolutional variational autoencoders (VAEs). This refined technique allows mapping latent values to input features, achieving performance comparable to trained black box VAEs. Additionally, the paper proposes a custom feature map visualization technique to analyze internal convolutional layers in VAEs and identify potential causes of poor reconstruction that could lead to dangerous traffic scenarios. Furthermore, the authors develop explanation and evaluation techniques for the internal dynamics and feature relevance of prediction networks, testing a long short-term memory (LSTM) network in computer vision to assess predictability and safety. The paper concludes by showcasing these methods on a VAE-LSTM world model predicting pedestrian perception in urban traffic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers aim to make autonomous driving systems more transparent and safe. They use special techniques called explainable AI to help us understand how the computers inside these systems think and make decisions. The main idea is to create a “glass box” that shows us what’s happening inside the computer as it makes predictions about things like where pedestrians might walk on the road. To do this, they use a type of neural network called a convolutional variational autoencoder (VAE) and refine its ability to explain itself. They also develop new ways to visualize the internal workings of these networks so we can better understand what’s going on inside. |
Keywords
» Artificial intelligence » Dimensionality reduction » Feature map » Lstm » Neural network » Variational autoencoder
