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Found 12 papers, 2 papers with code
RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model
Recent advancements in Multi-Modal Large Language models (MLLMs) have shown promising potential in enhancing the explainability as a driving agent by producing control predictions along with natural language explanations.
CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs
Corner case scenarios are an essential tool for testing and validating the safety of autonomous vehicles (AVs).
Effects of Explanation Specificity on Passengers in Autonomous Driving
In this paper, we investigate the effects of natural language explanations' specificity on passengers in autonomous driving.
Textual Explanations for Automated Commentary Driving
In this work, a state-of-the-art (SOTA) prediction and explanation model is thoroughly evaluated and validated (as a benchmark) on the new Sense--Assess--eXplain (SAX).
Explainable Action Prediction through Self-Supervision on Scene Graphs
This work explores scene graphs as a distilled representation of high-level information for autonomous driving, applied to future driver-action prediction.
From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actions
In the intelligent vehicle context, automated driving commentary can provide intelligible explanations about driving actions, thereby assisting a driver or an end-user during driving operations in challenging and safety-critical scenarios.
Explanations in Autonomous Driving: A Survey
With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations.
A Step Towards Exposing Bias in Trained Deep Convolutional Neural Network Models
We present Smooth Grad-CAM++, a technique which combines two recent techniques: SMOOTHGRAD and Grad-CAM++.
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models
With the intention to create an enhanced visual explanation in terms of visual sharpness, object localization and explaining multiple occurrences of objects in a single image, we present Smooth Grad-CAM++ \footnote{Simple demo: http://35. 238. 22. 135:5000/}, a technique that combines methods from two other recent techniques---SMOOTHGRAD and Grad-CAM++.
Efficient Machine Learning for Large-Scale Urban Land-Use Forecasting in Sub-Saharan Africa
Urbanization is a common phenomenon in developing countries and it poses serious challenges when not managed effectively.
EEG-based Communication with a Predictive Text Algorithm
Several changes occur in the brain in response to voluntary and involuntary activities performed by a person.
Deep Convolutional Neural Network for Plant Seedlings Classification
Agriculture is vital for human survival and remains a major driver of several economies around the world; more so in underdeveloped and developing economies.
