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SDC-Net++: End-to-End Crash Detection and Action Control for Self-Driving Car Deep-IoT-Based System.

Sensors (Basel, Switzerland)·2024

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SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System.

Mohammed Abdou¹, Hanan Ahmed Kamal²

¹Valeo Egypt, Cairo 12577, Egypt.

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|December 11, 2022

Summary

This study introduces SDC-Net, a deep learning and Internet of Things (IoT) system for autonomous driving. SDC-Net enhances safety through improved crash avoidance, path planning, and emergency braking using multitask learning.

Keywords:

IoT automatic emergency braking autonomous driving camera-cocoon computer vision crash avoidance deep learning multitask learning path planning system

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Area of Science:

Artificial Intelligence
Internet of Things (IoT)
Autonomous Driving Systems

Background:

Deep learning and IoT are increasingly vital in automotive applications, particularly for autonomous driving functionalities.
Essential autonomous driving functions include crash avoidance, path planning, and automatic emergency braking.
Trigger-action IoT platforms offer a simple yet effective method for receptive tasks.

Purpose of the Study:

To propose SDC-Net, an end-to-end hybrid system integrating deep learning and IoT for autonomous driving.
To develop a multitask neural network capable of outputting control actions for critical driving functions.
To create a benchmark dataset for robust testing across diverse driving scenarios and corner cases.

Main Methods:

An end-to-end deep learning IoT hybrid system, SDC-Net, was developed.
A multitask neural network was trained using various input representations from a camera-cocoon setup in the CARLA simulator.
A benchmark dataset was constructed to cover diverse driving scenarios and corner cases for comprehensive testing.

Main Results:

The SDC-Net system demonstrated effective control action outputs for crash avoidance, path planning, and automatic emergency braking.
Multitask learning utilizing a bird's eye view input representation significantly outperformed other representations.
Performance improvements included over 11.62% in precision, 9.43% in recall, 10.53% in F1-score, 6% in accuracy, and 25.84% in average MSE.

Conclusions:

The proposed SDC-Net system offers a robust and accurate solution for autonomous driving functionalities.
Bird's eye view representation in multitask learning is highly effective for improving autonomous driving performance.
The developed system and dataset contribute to safer and more reliable autonomous vehicle navigation.