Sep 10, 2024 · OmniTester is designed to generate realistic and diverse scenarios within a simulation environment, offering a robust solution for testing and evaluating AVs.
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Sep 10, 2024 · OmniTester is designed to generate realistic and diverse scenarios within a simulation environment, offering a robust solution for testing and evaluating AVs.
This study highlights the importance of refining AV driving algorithms and optimizing signal control systems to reduce environmental impacts and improve fuel ...
Sep 10, 2024 · OmniTester is a multimodal Large Language Model (LLM) based framework that fully leverages the extensive world knowledge and reasoning ...
Sep 10, 2024 · The paper describes a framework for using multimodal large language models to generate diverse driving scenarios for autonomous vehicle testing.
Oct 4, 2024 · Firstly, we designed a set of questions to guide multimodal large language models in comprehensively understanding driving scenarios, and based ...
Nov 5, 2024 · The results show that CogVLM surpasses existing autonomous driving capabilities in areas like scene understand- ing, prediction, and decision.
Building safe and intelligent Autonomous Vehicles (AVs) capable of human-like reasoning is a challenging problem, pushing the limits of computer vision.
This study shows the significance of MLLMs in advancing the analysis of naturalistic driving videos to improve safety-critical event detection.
This paper introduces LimSim++, an extended version of LimSim designed for the application of Multimodal Large Language Models ((M)LLMs) in autonomous driving.