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Found 19 papers, 7 papers with code
Model Predictive Optimized Path Integral Strategies
We generalize the derivation of model predictive path integral control (MPPI) to allow for a single joint distribution across controls in the control sequence.
Online Domain Adaptation for Occupancy Mapping
Further, with the use of high-fidelity driving simulators and real-world datasets, we demonstrate how parameters of 2D and 3D occupancy maps can be automatically adapted to accord with local spatial changes.
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models
We evaluate our method on a variety of generative models, including variational autoencoders and auto-regressive architectures.
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders
Discrete latent spaces in variational autoencoders have been shown to effectively capture the data distribution for many real-world problems such as natural language understanding, human intent prediction, and visual scene representation.
How Do We Fail? Stress Testing Perception in Autonomous Vehicles
This paper presents a method for characterizing failures of LiDAR-based perception systems for AVs in adverse weather conditions.
3D Radar Velocity Maps for Uncertain Dynamic Environments
This paper explores a Bayesian approach that captures our uncertainty in the map given training data.
Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments
We consider the problem of building continuous occupancy representations in dynamic environments for robotics applications.
Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environment
This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels.
Online Parameter Estimation for Human Driver Behavior Prediction
In this article, we show that online parameter estimation applied to the Intelligent Driver Model captures nuanced individual driving behavior while providing collision free trajectories.
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
Imitation learning is an approach for generating intelligent behavior when the cost function is unknown or difficult to specify.
Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments
We can use driving data collected over a long period of time to extract rich information about how vehicles behave in different areas of the roads.
Out-of-Distribution Detection for Automotive Perception
A mechanism to detect OOD samples is important for safety-critical applications, such as automotive perception, to trigger a safe fallback mode.
Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis
Deep learning models have become a popular choice for medical image analysis.
A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering
While the rules are governed by interpretable parameters of the driver model, these parameters are learned online from driving demonstration data using particle filtering.
CoCo Games: Graphical Game-Theoretic Swarm Control for Communication-Aware Coverage
We propose a novel framework for real-time communication-aware coverage control in networked robot swarms.
Optimal Transport for Distribution Adaptation in Bayesian Hilbert Maps
Parameters are one of the most critical components of machine learning models.
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning
Characterizing aleatoric and epistemic uncertainty can be used to speed up learning in a training environment, improve generalization to similar testing environments, and flag unfamiliar behavior in anomalous testing environments.
Renaissance Robot: Optimal Transport Policy Fusion for Learning Diverse Skills
Deep reinforcement learning (RL) is a promising approach to solving complex robotics problems.
Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility
Autonomous mobility is emerging as a new disruptive mode of urban transportation for moving cargo and passengers.
