Towards knowledge-driven autonomous driving
This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …
investigation highlights the limitations of current autonomous driving systems, in particular …
TrafficMCTS: A closed-loop traffic flow generation framework with group-based monte carlo tree search
Digital twins for intelligent transportation systems are currently attracting great interests, in
which generating realistic, diverse, and human-like traffic flow in simulations is a formidable …
which generating realistic, diverse, and human-like traffic flow in simulations is a formidable …
LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Driving
The emergence of Multimodal Large Language Models ((M) LLMs) has ushered in new
avenues in artificial intelligence, particularly for autonomous driving by offering enhanced …
avenues in artificial intelligence, particularly for autonomous driving by offering enhanced …
[HTML][HTML] Autonomous vehicle decision and control through reinforcement learning with traffic flow randomization
Y Lin, A Xie, X Liu - Machines, 2024 - mdpi.com
Most of the current studies on autonomous vehicle decision-making and control based on
reinforcement learning are conducted in simulated environments. The training and testing of …
reinforcement learning are conducted in simulated environments. The training and testing of …
[HTML][HTML] Fleet2D: A fast and light simulator for home robotics
A Sadhwani, H Badiozamani, T Agarwal… - 2023 - amazon.science
Home robots operate in diverse and dynamic environments, delivering a range of functions
that enhance utility. Many of these functions span extended periods, from weeks to months …
that enhance utility. Many of these functions span extended periods, from weeks to months …