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Autonomous Vehicle Controls using MATLAB and Simulink

A 3 month course designed to give students a hands on approach to learn and develop model based control strategies for driver assist systems.

12 weeks long course | 100% Online

Learn from leading experts in the industry

Project based learning with 2 industry level projects that learners can showcase on LinkedIn.

Learn Key Tools & Technologies MATLAB, Simulink

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Syllabus

This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects

Course Overview

  • The students will learn and develop model-based control strategies for driver-assistant systems.
  • During the coursework, the students will learn the following concepts:
    • Various control strategies used to develop automotive systems
    • Hands-on experience in developing algorithms and interfaces for driver-assistant technologies
    • Model-based system engineering
  • Also, they will be able to build their own model for level 2 autonomous vehicle features and add modifications to improve the system capabilities
  • The course will give an overview of the software tools such as MATLAB and Simulink which are widely used in the industry.
  • The students are exposed to the modern trends and standard practices being followed in the industry right now.
  • This course forms the foundation for anyone wanting to pursue a career in this domain.

Course Syllabus

On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.

Week 1 - Course Overview and Classical control

  • Course overview:
    • Introduction
    • Overview of Automotive Systems Engineering
    • Program Management – Systems Engineering
  • Classical Controls Theory Overview
    • Stability Pole Zeros
    • Transient Performance
    • Disturbance and Tracking
    • PID Systems
    • Gain Selection and Tuning
    • Examples Comparing P, PI, PD, PID

Week 2 - Longitudinal Controller Design

  • Longitudinal Dynamic Model
  • Aero Drag and Rolling Resistance
  • Linearizing Longitudinal Model
  • Controller Design in Simulink
  • Normal Cruise Control Project
  • Performance Analysis Using Step Response

Week 3 - Adaptive cruise control model

  • Design and Develop ACC Control Algorithm and Model in Simulink
    • Feature Overview: Implementation, Sensor Sets, etc.
    • Headway Control Model
    • Speed Control Model
    • Switching Logic in State Flow Techniques
    • Controller Design and Tuning
    • Performance Tuning using Feed-Forward Method

Week 4 - Advanced ACC - ACC Feature Modification

  • Add Additional Functionality to the Model to Improve ACC Performance.
    • CACC overview: Cooperative ACC Model
    • Logic Implementation
    • A Complete Model with Ego Vehicle and Target Vehicle in Simulink
    • Simulation Scenarios and MIL 

Week 5 - Lateral Control for Vehicles - Geometric Method

  • Geometric Control Methods
  • Pure Pursuit Controller 
  • Lane Keep System Using Pure Pursuit
  • Stanley Controller
  • LKS Using Stanley

Week 6 - Lateral Controller Model for Vehicles- Dynamic Modeling

  • Lateral Control Model Elements and Overview
  • Bicycle Model
  • Tire Model
  • State Equation for Lateral Control Model
  • Introduction to MPC
  • Controller Design using MPC
  • Integration and Modelling in MATLAB

Week 7 - Lane Centering Assist

  • Develop a Level 2 Model for Lane Centering Assist
    • Lane Center Assist Logic
    • Feature Boundary Diagram and Functions
    • Steering Path Polynomial
    • Mode Manager and Fault Manager Design
    • Switching Logic for Scenarios
    • Model in Simulink

Week 8 - Complete Level 2 Feature Model - Autopilot

  • Combine the Models Developed Previously into a Single-vehicle Model and Simulate Scenarios with all Active Features
    • Introduction to System Architecture
    • Introduction to Electronic Horizon, HD Maps

Week 9 - LCA Modification: Assisted Lane Biasing and Assisted Lane Change

  • Assisted Lane Biasing Logic and Implementation
  • Assisted Lane Change Logic
  • Path Planning for ALC
  • Path Planning Function with LCA Model

Week 10 - Combined Controller - 5 DOF

  • Combined Model of Lateral and Longitudinal Control
  • Vehicle Dynamic Derivation for State Matrices
  • State-Space Mathematics for 5 DOF System
  • Implement a Single Controller System in Simulink

Week 11 - Advanced Topics in Controls for Autonomous Driving- Part 1

  • Predictive Speed Assist
    • Introduction to Predictive Speed Assist and Intelligent Speed Assist
    • Curve Speed Control Derivation
    • Pseudo Code for PSA and ISA
    • Integration of PSA with Velocity Control Logic
  • Control for Roundabout Scenarios
  • Minimum Risk Manuevers

Week 12 - Advanced Topics in Controls for Autonomous Driving- Part 2

  • AV Special Applications
    • Off-road Mining
    • Logistics and Supply Chain
    • Agricultural Activities
    • Smart Mobility
  • AV Special ODs
    • Toll Gates
    • U-turns in Dead-end
  • Other Control Techniques
    • Cascade Control
    • Nonlinear MPC
    • Sliding Mode Control
  • Future Topics for Research
    • Deep Reinforcement Learning
    • Machine Learning Applications in AVs

Our courses have been designed by industry experts to help students achieve their dream careers

Industry Projects

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.

Design and Develop Adaptive Cruise Control Model in Simulink

In this project, the students will create a green wave traffic assist feature that recommends vehicle speeds such that the vehicle can be driven in the green wave and minimise stop time in traffic. The students will design and develop an ACC control algorithm and model in Simulink.

Develop an Integrated Automated Driving Model

In this project, the students will develop a level 2 model for lane centering assist and analyze the performance of different controller designs at different road/vehicle scenarios. They will gain hands on experience to develop an integrated automated driving model.

Our courses have been designed by industry experts to help students achieve their dream careers

Ratings & Reviews by Learners

Skill-Lync has received honest feedback from our learners around the globe.

Google Rating
4.6

Learn how to design self-driving cars with our Autonomous Vehicle Controls using MATLAB and Simulink course.

The primary goal of this 3-month autonomous vehicle course is designed to give students a hands-on approach to learning and developing model-based control strategies for driver-assist systems. 

There was an increase of 163% in the number of Electric Vehicles that were registered in India during the FY 2021 constituting 1.7% of the total petrol vehicles sold in the same year. This increase in EV sales has directly contributed to an increase in the number of ADAS-trained engineers in the ecosystem. 

Skill-Lync is the leading e-learning engineering platform that has created specially designed autonomous vehicle courses to cater to this growing demand. The fee structure for Skill-Lync’s autonomous vehicle certification courses is flexible. You can choose a plan from INR 7,000 to INR 15,000 as per your requirements.

Who Should Take This Course?

This certification course in autonomous vehicles forms the foundation for anyone wanting to pursue a career in this domain.

Students who want to Design and Develop Adaptive Cruise Control Model in Simulink and students who want to Develop an Integrated Automated Driving Model can ernoll in Skill-Lync’s autonomous vehicle course.

What Will You Learn in This Course?

This 12-week course on autonomous vehicle controls will train you in learning and developing model-based control strategies for driver-assistant systems. During this autonomous vehicle course, students will learn about,

  • Various control strategies are used to develop automotive systems, 
  • Hands-on experience in developing algorithms and interfaces for driver-assistant technologies
  • Model-based system engineering

Students will also be able to will be able to build their own model for level 2 autonomous vehicle features and add modifications to improve the system capabilities. The course will give an overview of the software tools such as MATLAB and Simulink which are widely used in the industry.

Skills You Will Gain

  • You will design and develop an ACC control algorithm and model in Simulink. 
  • You will learn how to develop a level 2 model for lane centring assist and analyze the performance of different controller designs in different road/vehicle scenarios
  • By working on real-time projects, you will become industry-ready.
  • You will gain industry experience in Simulink. 

Key Highlights of the Program

  • The duration of the course is 12 weeks.
  • Besides the course completion certificate for all participants, the top 5% of learners get a merit certificate.
  • You will get Individual Video Support, Group Video Support, Email Support, and Forum Support to clear your queries and doubts.
  • Real-time industry-relevant projects will make your learning purposeful.

Career Opportunities after Taking This Course

  • Senior software engineer 
  • ADAS test engineer
  • Engineering technical writer
  • Architect ADAS
  • ADAS EE feature owner

FAQs on Autonomous Vehicle Controls using MATLAB and Simulink

  1. Who can take up these autonomous vehicle certification courses?

Students and graduates with a technical background in electrical engineering and mechanical engineering can opt for this course.

  1. Is this an online autonomous vehicle course?

Yes, this is a 100% online course.

  1. What is an autonomous vehicle?

A conventional vehicle that has automated its driving parameters. Autonomous driving does not just include self-driving cars, it includes proximity sensors that warn the user when a vehicle is approaching your vehicle or cruise controls that ensure a static speed while driving on the highway.

  1. What is the fee for this autonomous vehicle online course?

The fee structure is flexible, and you can choose a plan that suits you. The basic plan would give you two months of access, the pro plan would give you four months of access, and the premium plan would give you lifetime access.

  1. How much can a machine learning engineer earn?

According to Glassdoor, the average salary of a machine learning engineer is INR 8 LPA. After completing this course, you can also earn this average pay, and you can expect more once you become an experienced professional.

  1. Is there any certificate for completing this Autonomous Vehicle Controls using MATLAB and Simulink course?

Yes, After completing this Autonomous Vehicle Controls using MATLAB and Simulink course, you shall be given a course completion certificate. The top 5% of the scorers will be given a merit certificate alongside the course completion certificate.

  1. Is there any technical support available for this autonomous vehicle course?

Yes, you can clear your queries with email and forum support.

Instructors profiles

Our courses are designed by leading academicians and experienced industry professionals.

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Our instructors are industry experts along with a passion to teach.

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4 years in the experience range

Instructors with 4 years extensive industry experience.

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Areas of expertise

  • RADAR

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