20 citations · 50 across the 27 of their papers we have counts for
20 papers · 1 filter
OpenSCvx: An Open-Source Modular and Extensible Nonlinear Trajectory Planning Package
Christopher R. Hayner, Griffin J. Norris, Fabio Spada +4
Trajectory optimization computes dynamically feasible motions that enable autonomous systems to accomplish complex tasks while satisfying operational and environmental constraints.…
Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation
Anubhav Vishwakarma, Bhaumik Mehta, Caleb Hsu +3
Inverse Reinforcement Learning (IRL) algorithms are powerful tools for learning from and generalizing expert demonstrations, but they often rely on unconstrained exploration, rende…
Surprise Potential as a Measure of Interactivity in Driving Scenarios
Wenhao Ding, Sushant Veer, Karen Leung +2
Validating the safety and performance of an autonomous vehicle (AV) requires benchmarking on real-world driving logs. However, typical driving logs contain mostly uneventful scenar…
Driving Everywhere with Large Language Model Policy Adaptation
Boyi Li, Yue Wang, Jiageng Mao +4
Adapting driving behavior to new environments, customs, and laws is a long-standing problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs)…
Refining Obstacle Perception Safety Zones via Maneuver-Based Decomposition
Sever Topan, Yuxiao Chen, Edward Schmerling +4
A critical task for developing safe autonomous driving stacks is to determine whether an obstacle is safety-critical, i.e., poses an imminent threat to the autonomous vehicle. Our…
Task-Aware Risk Estimation of Perception Failures for Autonomous Vehicles
Pasquale Antonante, Sushant Veer, Karen Leung +3
Safety and performance are key enablers for autonomous driving: on the one hand we want our autonomous vehicles (AVs) to be safe, while at the same time their performance (e.g., co…
HALO: Hazard-Aware Landing Optimization for Autonomous Systems
Christopher R. Hayner, Samuel C. Buckner, Daniel Broyles +3
With autonomous aerial vehicles enacting safety-critical missions, such as the Mars Science Laboratory Curiosity rover's landing on Mars, the tasks of automatically identifying and…
Learning Responsibility Allocations for Safe Human-Robot Interaction with Applications to Autonomous Driving
Ryan K. Cosner, Yuxiao Chen, Karen Leung +1
Drivers have a responsibility to exercise reasonable care to avoid collision with other road users. This assumed responsibility allows interacting agents to maintain safety without…
Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles
Sushant Veer, Karen Leung, Ryan Cosner +3
Autonomous vehicles must often contend with conflicting planning requirements, e.g., safety and comfort could be at odds with each other if avoiding a collision calls for slamming…
Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehicles
Alec Farid, Sushant Veer, Boris Ivanovic +2
In modern autonomy stacks, prediction modules are paramount to planning motions in the presence of other mobile agents. However, failures in prediction modules can mislead the down…
Interaction-Dynamics-Aware Perception Zones for Obstacle Detection Safety Evaluation
Sever Topan, Karen Leung, Yuxiao Chen +5
To enable safe autonomous vehicle (AV) operations, it is critical that an AV's obstacle detection module can reliably detect obstacles that pose a safety threat (i.e., are safety-c…
Semi-Supervised Trajectory-Feedback Controller Synthesis for Signal Temporal Logic Specifications
Karen Leung, Marco Pavone
There are spatio-temporal rules that dictate how robots should operate in complex environments, e.g., road rules govern how (self-driving) vehicles should behave on the road. Howev…
Towards Data-Driven Synthesis of Autonomous Vehicle Safety Concepts
Karen Leung, Andrea Bajcsy, Edward Schmerling +1
As safety-critical autonomous vehicles (AVs) will soon become pervasive in our society, a number of safety concepts for trusted AV deployment have recently been proposed throughout…
On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions
Karen Leung, Edward Schmerling, Mengxuan Zhang +4
Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is en…
Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions
Simon Schaefer, Karen Leung, Boris Ivanovic +1
To achieve seamless human-robot interactions, robots need to intimately reason about complex interaction dynamics and future human behaviors within their motion planning process. H…
Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach
Boris Ivanovic, Karen Leung, Edward Schmerling +1
Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive robot planning algorit…
Infusing Reachability-Based Safety into Planning and Control for Multi-agent Interactions
Xinrui Wang, Karen Leung, Marco Pavone
Within a robot autonomy stack, the planner and controller are typically designed separately, and serve different purposes. As such, there is often a diffusion of responsibilities w…
On Infusing Reachability-Based Safety Assurance within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions
Karen Leung, Edward Schmerling, Mo Chen +3
Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is en…
Generative Modeling of Multimodal Multi-Human Behavior
Boris Ivanovic, Edward Schmerling, Karen Leung +1
This work presents a methodology for modeling and predicting human behavior in settings with N humans interacting in highly multimodal scenarios (i.e. where there are many possible…
Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction
Edward Schmerling, Karen Leung, Wolf Vollprecht +1
This paper presents a method for constructing human-robot interaction policies in settings where multimodality, i.e., the possibility of multiple highly distinct futures, plays a c…