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Found 32 papers, 18 papers with code
InstantSplat: Unbounded Sparse-view Pose-free Gaussian Splatting in 40 Seconds
This pre-processing is usually conducted via a Structure-from-Motion (SfM) pipeline, a procedure that can be slow and unreliable, particularly in sparse-view scenarios with insufficient matched features for accurate reconstruction.
Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
High-definition (HD) maps have played an integral role in the development of modern autonomous vehicle (AV) stacks, albeit with high associated labeling and maintenance costs.
Parallelized Spatiotemporal Binding
While modern best practices advocate for scalable architectures that support long-range interactions, object-centric models are yet to fully embrace these architectures.
Driving Everywhere with Large Language Model Policy Adaptation
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).
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision
We present EmerNeRF, a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes.
Reinforcement Learning with Human Feedback for Realistic Traffic Simulation
This works aims to address this by developing a framework that employs reinforcement learning with human preference (RLHF) to enhance the realism of existing traffic models.
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
The field of trajectory forecasting has grown significantly in recent years, partially owing to the release of numerous large-scale, real-world human trajectory datasets for autonomous vehicles (AVs) and pedestrian motion tracking.
Language Conditioned Traffic Generation
In this work, we turn to language as a source of supervision for dynamic traffic scene generation.
Language-Guided Traffic Simulation via Scene-Level Diffusion
Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development.
Partial-View Object View Synthesis via Filtered Inversion
At inference, given one or more views of a novel real-world object, FINV first finds a set of latent codes for the object by inverting the generative model from multiple initial seeds.
DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles
To enable the joint optimization of AV stacks while retaining modularity, we present DiffStack, a differentiable and modular stack for prediction, planning, and control.
Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models
Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles.
Robust and Controllable Object-Centric Learning through Energy-based Models
Humans are remarkably good at understanding and reasoning about complex visual scenes.
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
Learning-based behavior prediction methods are increasingly being deployed in real-world autonomous systems, e. g., in fleets of self-driving vehicles, which are beginning to commercially operate in major cities across the world.
BITS: Bi-level Imitation for Traffic Simulation
We empirically validate our method, named Bi-level Imitation for Traffic Simulation (BITS), with scenarios from two large-scale driving datasets and show that BITS achieves balanced traffic simulation performance in realism, diversity, and long-horizon stability.
ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning
In this work, we present ScePT, a policy planning-based trajectory prediction model that generates accurate, scene-consistent trajectory predictions suitable for autonomous system motion planning.
Whose Track Is It Anyway? Improving Robustness to Tracking Errors With Affinity-Based Trajectory Prediction
This is typically caused by the propagation of errors from tracking to prediction, such as noisy tracks, fragments, and identity switches.
MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation
Recently, there has been tremendous progress in developing each individual module of the standard perception-planning robot autonomy pipeline, including detection, tracking, prediction of other agents' trajectories, and ego-agent trajectory planning.
Propagating State Uncertainty Through Trajectory Forecasting
As a result, perceptual uncertainties are not propagated through forecasting and predictions are frequently overconfident.
Injecting Planning-Awareness into Prediction and Detection Evaluation
Detecting other agents and forecasting their behavior is an integral part of the modern robotic autonomy stack, especially in safety-critical scenarios entailing human-robot interaction such as autonomous driving.
Sample-Efficient Safety Assurances using Conformal Prediction
When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial.
Rethinking Trajectory Forecasting Evaluation
Forecasting the behavior of other agents is an integral part of the modern robotic autonomy stack, especially in safety-critical scenarios with human-robot interaction, such as autonomous driving.
Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty
Reasoning about the future behavior of other agents is critical to safe robot navigation.
Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions
To achieve seamless human-robot interactions, robots need to intimately reason about complex interaction dynamics and future human behaviors within their motion planning process.
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.
MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control
Reasoning about human motion is a core component of modern human-robot interactive systems.
Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction
This paper presents a novel online framework for safe crowd-robot interaction based on risk-sensitive stochastic optimal control, wherein the risk is modeled by the entropic risk measure.
Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach
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 algorithms.
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation.
Ranked #2 on
Trajectory Prediction
on ETH
The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs
Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society.
BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning
Our Backward Reachability Curriculum (BaRC) begins policy training from states that require a small number of actions to accomplish the task, and expands the initial state distribution backwards in a dynamically-consistent manner once the policy optimization algorithm demonstrates sufficient performance.
Generative Modeling of Multimodal Multi-Human Behavior
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 highly-distinct futures).
Robotics Human-Computer Interaction
