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Found 6 papers, 2 papers with code
Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving
Beyond the dataset, we provide a simulation and evaluation framework that enables a planner's actions to be simulated in closed-loop to account for interactions with other traffic participants.
The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior
First, we found that these rules were enough for these models to achieve a high classification accuracy on the dataset.
Neural Network-based Control for Multi-Agent Systems from Spatio-Temporal Specifications
We propose a framework for solving control synthesis problems for multi-agent networked systems required to satisfy spatio-temporal specifications.
Rule-based Optimal Control for Autonomous Driving
We develop optimal control strategies for Autonomous Vehicles (AVs) that are required to meet complex specifications imposed by traffic laws and cultural expectations of reasonable driving behavior.
Autonomous Driving
Robotics
Systems and Control
Systems and Control
Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints
We propose a framework based on Recurrent Neural Networks (RNNs) to determine an optimal control strategy for a discrete-time system that is required to satisfy specifications given as Signal Temporal Logic (STL) formulae.
Control from Signal Temporal Logic Specifications with Smooth Cumulative Quantitative Semantics
We present a framework to synthesize control policies for nonlinear dynamical systems from complex temporal constraints specified in a rich temporal logic called Signal Temporal Logic (STL).
Systems and Control
