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Found 9 papers, 2 papers with code
Behavioral Cloning Models Reality Check for Autonomous Driving
How effective are recent advancements in autonomous vehicle perception systems when applied to real-world autonomous vehicle control?
Optical Diffusion Models for Image Generation
Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution.
HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model
Autonomous driving is a complex task which requires advanced decision making and control algorithms.
Nonlinear Processing with Linear Optics
In this study, we present a novel framework that uses multiple scattering that is capable of synthesizing programmable linear and nonlinear transformations concurrently at low optical power by leveraging the nonlinear relationship between the scattering potential, represented by data, and the scattered field.
Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach
The dynamic nature of driving environments and the presence of diverse road users pose significant challenges for decision-making in autonomous driving.
Forward-Forward Training of an Optical Neural Network
Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations.
Prediction Based Decision Making for Autonomous Highway Driving
This paper proposes a Prediction-based Deep Reinforcement Learning (PDRL) decision-making model that considers the manoeuvre intentions of surrounding vehicles in the decision-making process for highway driving.
Nonlinear Optical Data Transformer for Machine Learning
A dataset is encoded digitally on the spectrum of a femtosecond pulse which is then launched in the waveguide.
Scalable Optical Learning Operator
Today's heavy machine learning tasks are fueled by large datasets.
