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Found 7 papers, 1 papers with code
Towards Robust Car Following Dynamics Modeling via Blackbox Models: Methodology, Analysis, and Recommendations
The selection of the target variable is important while learning parameters of the classical car following models like GIPPS, IDM, etc.
Reframing Offline Reinforcement Learning as a Regression Problem
The study proposes the reformulation of offline reinforcement learning as a regression problem that can be solved with decision trees.
A formal process of hierarchical functional requirements development for Set-Based Design
Set-Based Design is a promising approach to complex systems design as it supports alternative exploration and gradual uncertainty reduction.
Gaussian Process-based Model Predictive Controller for Connected Vehicles with Uncertain Wireless Channel
In this paper, we present a data-driven Model Predictive Controller that leverages a Gaussian Process to generate optimal motion policies for connected autonomous vehicles in regions with uncertainty in the wireless channel.
SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction
Safe navigation of autonomous agents in human centric environments requires the ability to understand and predict motion of neighboring pedestrians.
ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers
In this paper, we consider the problem of creating a safe-by-design Rectified Linear Unit (ReLU) Neural Network (NN), which, when composed with an arbitrary control NN, makes the composition provably safe.
Towards Improved Testing For Deep Learning
The growing use of deep neural networks in safety-critical applications makes it necessary to carry out adequate testing to detect and correct any incorrect behavior for corner case inputs before they can be actually used.
