Software Architecture for Autonomous Vehicles using MATLAB Simulink

<div class="section abstract"><div class="htmlview paragraph">Autonomous vehicles utilise sensors, control systems and machine learning to independently navigate and operate through their surroundings, offering improved road safety, traffic management and enhanced mobility. This paper details the development, software architecture and simulation of control algorithms for key functionalities in a model that approaches Level 2 autonomy, utilising MATLAB Simulink and IPG CarMaker. The focus is on four critical areas: Autonomous Emergency Braking (AEB), Adaptive Cruise Control (ACC), Lane Detection (LD) and Traffic Object Detection. Also, the integration of low-level PID controllers for precise steering, braking and throttle actuation, ensures smooth and responsive vehicle behaviour. The hardware architecture is built around the Nvidia Jetson Nano and multiple Arduino Nano microcontrollers, each responsible for controlling specific actuators within the drive-by-wire system, which includes the steering, brake and throttle actuators. Communication between these components is facilitated through the CAN protocol, which ensures accurate and reliable data transfer essential for real-time decision-making. AEB achieves precise emergency braking and enhances driver comfort through the use of PID controllers, while ACC leverages radar data to maintain a safe distance from the vehicle ahead. LD employs the Hough Transform algorithm for accurate road edge detection. Furthermore, a trained neural network within the system identifies and responds to traffic signals, signage, pedestrians and vehicles. The camera interfaces directly with the Jetson Nano, while radar data is shared with the IMU through a dedicated CAN bus. This integrated approach represents a significant advancement in autonomous vehicle control, thus contributing to enhanced safety, comfort and reliability for both drivers and passengers. This software architecture is designed based on aBaja 2024 competition and according to its rules, regulations, requirements and specifications, the controllers and simulations were designed.</div></div>

Authors

Institutions

Publication Details

Journal
SAE technical papers on CD-ROM/SAE technical paper series
Published
2025-02-06
DOI
https://doi.org/10.4271/2025-28-0190
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Software Architecture for Autonomous Vehicles using MATLAB Simulink

Anwar Sadique, Merlin Thomas, Andrés Sierra M., Tessa Ann Josy et al.
SAE technical papers on CD-ROM/SAE technical paper series
Autonomous Vehicle Technology and Safety
article

Software Architecture for Autonomous Vehicles using MATLAB Simulink

Anwar Sadique, Merlin Thomas, Andrés Sierra M., Tessa Ann Josy, Sreeraj Vr
article en

Abstract

<div class="section abstract"><div class="htmlview paragraph">Autonomous vehicles utilise sensors, control systems and machine learning to independently navigate and operate through their surroundings, offering improved road safety, traffic management and enhanced mobility. This paper details the development, software architecture and simulation of control algorithms for key functionalities in a model that approaches Level 2 autonomy, utilising MATLAB Simulink and IPG CarMaker. The focus is on four critical areas: Autonomous Emergency Braking (AEB), Adaptive Cruise Control (ACC), Lane Detection (LD) and Traffic Object Detection. Also, the integration of low-level PID controllers for precise steering, braking and throttle actuation, ensures smooth and responsive vehicle behaviour. The hardware architecture is built around the Nvidia Jetson Nano and multiple Arduino Nano microcontrollers, each responsible for controlling specific actuators within the drive-by-wire system, which includes the steering, brake and throttle actuators. Communication between these components is facilitated through the CAN protocol, which ensures accurate and reliable data transfer essential for real-time decision-making. AEB achieves precise emergency braking and enhances driver comfort through the use of PID controllers, while ACC leverages radar data to maintain a safe distance from the vehicle ahead. LD employs the Hough Transform algorithm for accurate road edge detection. Furthermore, a trained neural network within the system identifies and responds to traffic signals, signage, pedestrians and vehicles. The camera interfaces directly with the Jetson Nano, while radar data is shared with the IMU through a dedicated CAN bus. This integrated approach represents a significant advancement in autonomous vehicle control, thus contributing to enhanced safety, comfort and reliability for both drivers and passengers. This software architecture is designed based on aBaja 2024 competition and according to its rules, regulations, requirements and specifications, the controllers and simulations were designed.</div></div>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
Government Medical College (IN)
Openalex Percentile: Top 98%
Autonomous Vehicle Technology and Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.