ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models [IEEE ITSC 2024] [supporting dataset]

Rajkumar, Ragunathan (Raj); Sahu, Nishad; Sural, Shounak

i

ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models [IEEE ITSC 2024] [supporting dataset]

Dataset

2024-09-01
By Rajkumar, Ragunathan (Raj) ; Sahu, Nishad ; Sural, Shounak

File Language:

English

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Creators:

Rajkumar, Ragunathan (Raj) ; Sahu, Nishad ; Sural, Shounak
Corporate Creators:

Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:

Autonomous Vehicles Connected Vehicles Machine Learning Vehicle Safety
Resource Type:

Dataset
Right Statement:

Open Access; http://creativecommons.org/publicdomain/zero/1.0
Geographical Coverage:

United States
Corporate Publisher:

Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
Abstract:

In this work, we introduce a dataset called DrivingContexts for the detection of relevant driving contexts for autonomous driving. Additionally, we propose the use of vision language models such as LLaVa and ViLT with zero-shot and few-shot approaches, to solve the problem of detecting such contexts. With our approach, we reduce the need for fully supervised training on large annotated datasets to detect these contexts and also the need for hand-crafted approaches to understand specific contexts of importance.
Content Notes:

This item is made available under the terms of the Creative Commons Attribution 1.0 Universal (CC0 1.0) license https://creativecommons.org/publicdomain/zero/1.0/. External Repository Note: This dataset additional data and software in a GitHub repository. This repository is accessible online: https://github.com/ssuralcmu/ContextVLM.git
Format:

ZIP
Funding:

69A3552344811
Collection(s):

University Transportation Centers Autonomous Vehicles
Main Document Checksum:

urn:sha-512:afef8c9690e9b4c1c77408b79bb8ed04bf726739f46816bf06cb10506d2cb4e71245f6fb32de2a1fa398a7e5deebd418156f1bde666f6c6e49e6a014ef635455
Download URL:

https://rosap.ntl.bts.gov/view/dot/87498/dot_87498_DS1.zip
File Type:

[ZIP - 2.22 MB ]

Safety Assurance and Demonstration of Connected Autonomous Vehicles

This proposed larger-scale effort aims to re-define and demonstrate the vision of full autonomy to one of safe autonomy, where a learning-enabled syst ...

Rajkumar, Ragunathan (Raj)

;

Sahu, Nishad

File Language:

English

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