Learning-based methods of perception and navigation for ground vehicles in unstructured environments: A review
DC Guastella, G Muscato - Sensors, 2020 - mdpi.com
The problem of autonomous navigation of a ground vehicle in unstructured environments is
both challenging and crucial for the deployment of this type of vehicle in real-world …
both challenging and crucial for the deployment of this type of vehicle in real-world …
Multi-sensor fusion in automated driving: A survey
Z Wang, Y Wu, Q Niu - Ieee Access, 2019 - ieeexplore.ieee.org
With the significant development of practicability in deep learning and the ultra-high-speed
information transmission rate of 5G communication technology will overcome the barrier of …
information transmission rate of 5G communication technology will overcome the barrier of …
Trajectory forecasts in unknown environments conditioned on grid-based plans
N Deo, MM Trivedi - arXiv preprint arXiv:2001.00735, 2020 - arxiv.org
We address the problem of forecasting pedestrian and vehicle trajectories in unknown
environments, conditioned on their past motion and scene structure. Trajectory forecasting is …
environments, conditioned on their past motion and scene structure. Trajectory forecasting is …
Integrating Dijkstra's algorithm into deep inverse reinforcement learning for food delivery route planning
In China, rapid development of online food delivery brings massive orders, which relies
heavily on deliverymen riding e-bikes. In practice, actual delivery routes of most orders are …
heavily on deliverymen riding e-bikes. In practice, actual delivery routes of most orders are …
A survey on path planning for autonomous ground vehicles in unstructured environments
N Wang, X Li, K Zhang, J Wang, D Xie - Machines, 2024 - mdpi.com
Autonomous driving in unstructured environments is crucial for various applications,
including agriculture, military, and mining. However, research in unstructured environments …
including agriculture, military, and mining. However, research in unstructured environments …
Deep inverse reinforcement learning for behavior prediction in autonomous driving: Accurate forecasts of vehicle motion
Accurate behavior anticipation is essential for autonomous vehicles when navigating in
close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in …
close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in …
Energy-based legged robots terrain traversability modeling via deep inverse reinforcement learning
This work reports ondeveloping a deep inverse reinforcement learning method for legged
robots terrain traversability modeling that incorporates both exteroceptive and proprioceptive …
robots terrain traversability modeling that incorporates both exteroceptive and proprioceptive …
Trajectory planning for autonomous vehicles using hierarchical reinforcement learning
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making
Aerial cinematography is revolutionizing industries that require live and dynamic camera
viewpoints such as entertainment, sports, and security. However, safely piloting a drone …
viewpoints such as entertainment, sports, and security. However, safely piloting a drone …
Deep learning for vision-based prediction: A survey
A Rasouli - arXiv preprint arXiv:2007.00095, 2020 - arxiv.org
Vision-based prediction algorithms have a wide range of applications including autonomous
driving, surveillance, human-robot interaction, weather prediction. The objective of this …
driving, surveillance, human-robot interaction, weather prediction. The objective of this …