Interpretable self-aware neural networks for robust trajectory prediction
M Itkina, M Kochenderfer - Conference on Robot Learning, 2023 - proceedings.mlr.press
Although neural networks have seen tremendous success as predictive models in a variety
of domains, they can be overly confident in their predictions on out-of-distribution (OOD) …
of domains, they can be overly confident in their predictions on out-of-distribution (OOD) …
How do we fail? stress testing perception in autonomous vehicles
Autonomous vehicles (AVs) rely on environment perception and behavior prediction to
reason about agents in their surroundings. These perception systems must be robust to …
reason about agents in their surroundings. These perception systems must be robust to …
Occlusion-aware crowd navigation using people as sensors
Autonomous navigation in crowded spaces poses a challenge for mobile robots due to the
highly dynamic, partially observable environment. Occlusions are highly prevalent in such …
highly dynamic, partially observable environment. Occlusions are highly prevalent in such …
Dynamics-aware spatiotemporal occupancy prediction in urban environments
Detection and segmentation of moving obstacles, along with prediction of the future
occupancy states of the local environment, are essential for autonomous vehicles to …
occupancy states of the local environment, are essential for autonomous vehicles to …
SCOPE: Stochastic Cartographic Occupancy Prediction Engine for Uncertainty-Aware Dynamic Navigation
This article presents a family of Stochastic Cartographic Occupancy Prediction Engines
(SCOPEs) that enable mobile robots to predict the future states of complex dynamic …
(SCOPEs) that enable mobile robots to predict the future states of complex dynamic …
Self-supervised Multi-future Occupancy Forecasting for Autonomous Driving
Environment prediction frameworks are critical for the safe navigation of autonomous
vehicles (AVs) in dynamic settings. LiDAR-generated occupancy grid maps (L-OGMs) offer a …
vehicles (AVs) in dynamic settings. LiDAR-generated occupancy grid maps (L-OGMs) offer a …
Vehicle motion forecasting using prior information and semantic-assisted occupancy grid maps
R Asghar, M Diaz-Zapata… - 2023 IEEE/RSJ …, 2023 - ieeexplore.ieee.org
Motion prediction is a challenging task for autonomous vehicles due to uncertainty in the
sensor data, the non-deterministic nature of future, and complex behavior of agents. In this …
sensor data, the non-deterministic nature of future, and complex behavior of agents. In this …
Neural world models for computer vision
A Hu - arXiv preprint arXiv:2306.09179, 2023 - arxiv.org
Humans navigate in their environment by learning a mental model of the world through
passive observation and active interaction. Their world model allows them to anticipate what …
passive observation and active interaction. Their world model allows them to anticipate what …
Lopr: Latent occupancy prediction using generative models
Environment prediction frameworks are integral for autonomous vehicles, enabling safe
navigation in dynamic environments. LiDAR generated occupancy grid maps (L-OGMs) offer …
navigation in dynamic environments. LiDAR generated occupancy grid maps (L-OGMs) offer …
Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy
Autonomous agents rely on sensor data to construct representations of their environments,
essential for predicting future events and planning their actions. However, sensor …
essential for predicting future events and planning their actions. However, sensor …