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Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment.

¹Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China.

Computational Intelligence and Neuroscience

|June 28, 2022

Summary

This study introduces an assisted driving system using machine vision and deep learning on a Jetson Nano platform. The system effectively plans paths and tracks targets, ensuring safe following distances and emergency braking.

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Advanced Driver-Assistance Systems (ADAS) require sophisticated perception and control.
Real-time decision-making and path planning are crucial for autonomous navigation.

Purpose of the Study:

To develop and evaluate an assisted driving system using a Jetson Nano embedded platform.
To implement a robust system capable of path planning, target tracking, and emergency maneuvers.

Main Methods:

Vehicle dynamics modeling under multiconditional assumptions.
Model Predictive Control (MPC) for path planning and tracking.
Finite State Machine (FSM) for behavioral decision-making.
Machine vision and deep learning for environmental perception.

Main Results:

The system successfully planned optimal paths with obstacle avoidance using MPC.
Autonomous vehicles smoothly tracked replanned paths for lane change overtaking.
Simulation results demonstrated accurate speed control and maintenance of safe distances, even during deceleration and on curves.

Conclusions:

The proposed system meets ADAS demands by integrating advanced algorithms on an embedded platform.
The system exhibits reliable performance in dynamic scenarios, including emergency braking and curve tracking.
The developed assisted driving system is suitable for operational ranges within 50 meters.