Challenges in Low Infrastructure and Off-Road Automated Driving

Carruth, Daniel W.; Walden, Clayton T.; Goodin, C.; Fuller, Sara C.

doi:10.1109/metrocad56305.2022.00008

Cited by 3 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…YOLO with adaptive frame control has been used for real-time object detection in AI-embedded systems [ 115 ]. In the case of navigation, the active learning algorithm [ 116 ] shows good potential for low infrastructure and the off-road environment for automated driving [ 117 ].…”

Section: Discussionmentioning

confidence: 99%

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Islam¹,

Nabi²,

Ball³

2022

Sensors

View full text Add to dashboard Cite

When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

show abstract

Section: Discussionmentioning

confidence: 99%

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Islam¹,

Nabi²,

Ball³

2022

Sensors

View full text Add to dashboard Cite

show abstract

TerrainSense: Vision-Driven Mapless Navigation for Unstructured Off-Road Environments

Hassan,

Sharma,

Madjid

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

AI-Enhanced Proactive Model Predictive Control for Off-Road Autonomous Vehicles

Bhosale,

Gupta,

Kumar

et al. 2024

SAE Technical Paper Series

View full text Add to dashboard Cite

<div class="section abstract"> <div class="htmlview paragraph">This paper investigates the use of artificial intelligence (AI) to enhance autonomous vehicle (AV) controls in unstructured off-road environments. Recent research developments address the utilization of AI and deep learning (DL) models to share information with AVs, enabling intelligent navigation in urban environments. The suggested research allows AVs to navigate in an off-road environment by utilizing AI-predicted trajectories while avoiding obstacles. Our method aims to enhance vehicle autonomy, enabling it to move promptly and responsively while interacting with other vehicles in an off-road environment. We demonstrate the efficacy of AI-driven trajectory predictions through comprehensive simulation validations. Moreover, the research demonstrates the use of AI prediction to construct a proactive Model Predictive Controller (MPC) for off-road autonomous vehicles. Simulation studies and comparisons with existing approaches validate the effectiveness and advantages of the proposed approach for off-road autonomous vehicle controls.</div> </div>

show abstract

Challenges in Low Infrastructure and Off-Road Automated Driving

Cited by 3 publications

References 42 publications

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

TerrainSense: Vision-Driven Mapless Navigation for Unstructured Off-Road Environments

AI-Enhanced Proactive Model Predictive Control for Off-Road Autonomous Vehicles

Contact Info

Product

Resources

About