May 30, 2017 · Multisensory polices are known to enhance both state estimation and target tracking. However, in the space of end-to-end sensorimotor control, ...

Multimodal DRL aims to leverage the availability of multiple, potentially imperfect, sensor inputs to improve learned policy. Most autonomous driving vehicles ...

Multimodal DRL aims to leverage the availability of multiple, potentially imperfect, sensor inputs to improve learned policy. Most autonomous driving vehicles ...

We proposed a multimodal end-to-end policy based on deep reinforcement learning (DRL) that leverages sensor fusion to reduced performance drops in noisy ...

This work proposes a novel stochastic regularization technique, called Sensor Dropout, to robustify multimodal sensor policy learning outcomes and shows ...

We propose a novel multi-modal policy fusion method for end-to-end autonomous driving. We use VCNet to select a primary decision to reduce the impact of fault ...

Apr 23, 2022 · The idea behind the encoder network is to force the feature map into representing the scene by learning a pixel wise semantic segmentation.

On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later ...

Oct 22, 2024 · In this work, we present a data-driven simulation and training engine capable of learning end-to-end autonomous vehicle control policies ...

Missing: Navigation. | Show results with:Navigation.