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Found 11 papers, 9 papers with code
Explore until Confident: Efficient Exploration for Embodied Question Answering
We consider the problem of Embodied Question Answering (EQA), which refers to settings where an embodied agent such as a robot needs to actively explore an environment to gather information until it is confident about the answer to a question.
Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction
We propose the use of evidential deep learning to estimate the epistemic uncertainty over a low-dimensional, interpretable latent space in a trajectory prediction setting.
LOPR: Latent Occupancy PRediction using Generative Models
Environment prediction frameworks are integral for autonomous vehicles, enabling safe navigation in dynamic environments.
Occlusion-Aware Crowd Navigation Using People as Sensors
To the best of our knowledge, this work is the first to use social occlusion inference for crowd navigation.
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.
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.
Multi-Agent Variational Occlusion Inference Using People as Sensors
We propose an occlusion inference method that characterizes observed behaviors of human agents as sensor measurements, and fuses them with those from a standard sensor suite.
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.
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.
Attention Augmented ConvLSTM for Environment Prediction
Safe and proactive planning in robotic systems generally requires accurate predictions of the environment.
Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning
A key challenge for autonomous driving is safe trajectory planning in cluttered, urban environments with dynamic obstacles, such as pedestrians, bicyclists, and other vehicles.
