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Related Experiment Video

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Operation of the Collaborative Composite Manufacturing CCM System

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Published on: October 1, 2019

Real-time path planning for autonomous vehicle off-road driving.

Ethery Ramirez-Robles¹, Oleg Starostenko¹, Vicente Alarcon-Aquino¹

¹Department of Computing, Electronics, and Mechatronics, Universidad de las Américas-Puebla, Puebla, Mexico.

Peerj. Computer Science

|August 15, 2024

Summary

This summary is machine-generated.

This study introduces a novel approach for autonomous off-road driving using semantic segmentation and real-time path planning. The system achieves high precision and faster computational times for navigation assistance.

Keywords:

Machine vision Navigation path planning Off-road autonomous driving Semantic image segmentation

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Autonomous driving is a rapidly advancing field with significant societal and economic benefits.
Fully autonomous off-road navigation remains a challenge due to complex terrain and perception requirements.
Existing autonomous vehicle (AV) systems require sophisticated data processing for sensing, mapping, localization, and path planning.

Purpose of the Study:

To propose an original approach for autonomous off-road driving by integrating semantic segmentation and real-time route planning.
To design a modular framework for assistive driving in off-road scenarios, optimized for resource-constrained devices.
To enhance scene understanding and path prediction capabilities for off-road autonomous navigation.

Main Methods:

A deep neural network performs semantic segmentation on Red-Green-Blue (RGB) camera images.
A traversability module fuses Light Detection And Ranging (LiDAR) data with segmentation results to create an occupancy grid map.
The Rapidly-exploring Random Tree (RRT) algorithm is adapted for real-time path planning.

Main Results:

The proposed semantic segmentation approach achieved 85.9% precision on the Freiburg Forest Dataset (FFD) and 79.5% on the RELLIS-3D dataset.
The system demonstrates faster computational times for path planning compared to existing methods.
The modular framework effectively provides scene understanding for autonomous navigation.

Conclusions:

The developed approach offers a promising solution for autonomous off-road navigation.
The integration of semantic segmentation and RRT-based path planning enhances AV performance in challenging environments.
The system's efficiency makes it suitable for resource-constrained off-road autonomous driving applications.