8 citations · 16 across the 8 of their papers we have counts for
10 papers
Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving
Napat Karnchanachari, Dimitris Geromichalos, Kok Seang Tan +8
Machine Learning (ML) has replaced traditional handcrafted methods for perception and prediction in autonomous vehicles. Yet for the equally important planning task, the adoption o…
The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior
Bassam Helou, Aditya Dusi, Anne Collin +7
Autonomous vehicles must balance a complex set of objectives. There is no consensus on how they should do so, nor on a model for specifying a desired driving behavior. We created a…
Rule-based Evaluation and Optimal Control for Autonomous Driving
Wei Xiao, Noushin Mehdipour, Anne Collin +4
We develop optimal control strategies for autonomous vehicles (AVs) that are required to meet complex specifications imposed as rules of the road (ROTR) and locally specific cultur…
Neural Network-based Control for Multi-Agent Systems from Spatio-Temporal Specifications
Suhail Alsalehi, Noushin Mehdipour, Ezio Bartocci +1
We propose a framework for solving control synthesis problems for multi-agent networked systems required to satisfy spatio-temporal specifications. We use Spatio-Temporal Reach and…
Rule-based Optimal Control for Autonomous Driving
Wei Xiao, Noushin Mehdipour, Anne Collin +4
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…
Specifying User Preferences using Weighted Signal Temporal Logic
Noushin Mehdipour, Cristian-Ioan Vasile, Calin Belta
We extend Signal Temporal Logic (STL) to enable the specification of importance and priorities. The extension, called Weighted STL (wSTL), has the same qualitative (Boolean) semant…
Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints
Wenliang Liu, Noushin Mehdipour, Calin Belta
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 give…
Average-based Robustness for Continuous-Time Signal Temporal Logic
Noushin Mehdipour, Cristian-Ioan Vasile, Calin Belta
We propose a new robustness score for continuous-time Signal Temporal Logic (STL) specifications. Instead of considering only the most severe point along the evolution of the signa…
Control from Signal Temporal Logic Specifications with Smooth Cumulative Quantitative Semantics
Iman Haghighi, Noushin Mehdipour, Ezio Bartocci +1
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 Lo…
Arithmetic-Geometric Mean Robustness for Control from Signal Temporal Logic Specifications
Noushin Mehdipour, Cristian-Ioan Vasile, Calin Belta
We present a new average-based robustness score for Signal Temporal Logic (STL) and a framework for optimal control of a dynamical system under STL constraints. By averaging the sc…