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Camera-LiDAR Multi-Level Sensor Fusion for Target Detection at the Network Edge.

Javier Mendez^1,2, Miguel Molina^1,2, Noel Rodriguez²

¹Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This study introduces a Multi-Level Sensor Fusion model for autonomous vehicles, optimizing target detection on edge devices like Google Coral TPUs. It achieves high accuracy while reducing latency and memory usage, overcoming hardware limitations.

Keywords:

LiDAR sensor camera sensor deep learning edge computing sensor fusion target detection

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Area of Science:

Computer Vision
Robotics
Machine Learning

Background:

Autonomous vehicles require robust target detection systems to overcome environmental and sensor challenges.
Sensor fusion, particularly using LiDAR and cameras, is crucial for accurate depth and shape perception.
Current algorithms often neglect hardware constraints like limited computing power and high latency in vehicles.

Purpose of the Study:

To propose an edge-computing solution for target detection in autonomous vehicles.
To address hardware limitations by utilizing Tensor Processing Unit (TPU) devices.
To develop an accurate and efficient target detection model optimized for edge deployment.

Main Methods:

Implemented a Multi-Level Sensor Fusion model optimized for edge devices.
Utilized Google Coral TPU for hardware acceleration of machine learning algorithms.
Evaluated the model's performance on the KITTI dataset.

Main Results:

Achieved high accuracy in target detection.
Significantly reduced memory consumption compared to traditional methods.
Lowered system latency, crucial for real-time autonomous driving.

Conclusions:

Edge computing with TPUs offers a viable solution for real-time target detection in autonomous vehicles.
The proposed Multi-Level Sensor Fusion model demonstrates effectiveness on resource-constrained edge devices.
This approach enhances the robustness and efficiency of autonomous driving systems.