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.

  • Domain : ELECTRICAL
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A Quick Overview

Accelerator, Brake and Clutch- the ABC’s that are taught to every newbie learning how to drive. But then, certain companies came in and said, what if we give you the experience of being in a car without having to drive or without even having another person driving it? This led to the invention of the idea of Autonomous Vehicles. (There is definitely more to it)

While Autonomous Vehicles are made up of many small parts that gives the car the credibility of being “autonomous” one aspect of it is it’s control system. Once the information is perceived the vehicle needs to respond to it. This is done by the controls of the vehicle, which mainly include the acceleration, brakes and steering wheel.  

But how does the vehicle do this? If we want autonomous vehicles to be  a reality we will need the controls of the vehicle to be super efficient and responsive. To understand how we can make this happen and also gain knowledge on the controls of an autonomous vehicle, we at skill lync have introduced a course specifically dedicated to autonomous vehicle controls. 

The course is spread across a 12 week program , where each week we teach you something new and exciting. Other than that, to help you have a clear and better understanding of the topic each week is followed by two practical sessions and a challenge and the end of it. This will help you get a hands on experience on your theoretical knowledge. Finally, you will be given 2 projects that you will be asked to work on during the course period, which you can add to your CV’s and apply what you  have learned in something that they build of your own. 

To know more about the syllabus and the projects, scroll away! 


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Course Syllabus

1Course Overview and Classical control

  • Course overview:
    • Introduction to all the topics
    • Motivation and why to learn them
    • 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

2Longitudinal Controller design

  • Longitudinal dynamic model
  • Aero drag and rolling resistance
  • Linearizing longitudinal model
  • Controller design in Simulink
  • Normal cruise control project
  • Performance analysis using a step response

3Adaptive 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

4Advanced ACC – modifying the ACC feature to next level

Add additional functionality to the model to improve ACC performance.

  • The CACC overview: Cooperative acc model
  • Logic implementation
  • Complete model with ego vehicle and target vehicle in Simulink
  • Simulation scenarios and MIL 

5Lateral control for vehicles – Geometric method

  • Geometric control methods
  • Pure pursuit controller 
  • Lane keep system using Pure pursuit.
  • Stanley controller
  • LKS using Stanley.

6Lateral 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

7Lane Centering Assist

  • Develop the level 2 model for lane cantering assist 
    • Lane centre assist logic
    • Feature boundary diagram and functions
    • Steering path polynomial
    • Mode manager and fault manager design
    • Switching logic for scenarios
    • Model in Simulink

8Complete Level 2 feature model - Autopilot

  • Combine the models developed previously into a single vehicle model and simulate scenarios with all the features active.
    • Introduction to system architecture
    • Intro to electronic horizon, HD maps

9LCA modification: Assisted lance biasing and Assisted lane change

  • Assisted lane biasing logic and implementation
  • Assisted lane change logic
  • Path planning for ALC
  • Implement path planning function with LCA model

10Combined Controller – 5 DOF

  • Introduce a combined model of lateral + longitudinal control
  • Vehicle dynamic derivation for state matrices
  • State space mathematics for 5 DOF system
  • Implement a single controller system in simulink

11Advanced 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 round about scenarios
  • Minimum risk manuevers

12Advanced topics in controls for autonomous driving - part 2

  • AV special applications
    • Off roach mining
    • Logistics and supply chain
    • Agricultural activities
    • Smart mobility
  • AV special ODs
    • Toll gates
    • U-turns in dead end
  • Other control techniques
    • Cascade control
    • Non linear mpc
    • Sliding mode control
  • Future topics for research
    • Deep reinforcement learning
    • Machine learning applications in AVs


Projects Overview

Project 1

Highlights

A project to implement a Driver assist feature for Green wave traffic assist. Green wave occurs when a series of traffic lights are coordinated to allow a continuous traffic flow over several intersections. green wave traffic assist is a feature which recommends vehicle speeds such that the vehicle can be run in the green wave and minimize stop time in traffic.

Key Highlights:

  • Use algorithmic approach of thinking
  • Use design thinking define requirements and implement algorithm
  • Use a simple kinematics concept to implement algorithms.
  • Gain proficient experience in Matlab scripting
  • Use matlab to generate simulation results.

Deliverables:

  • Show a flowchart diagram for the algorithm’s pseudocode.
  • Plot Green wave recommended velocity vs distance to traffic light.
  • Comments and comparison of output graphs\
  • Plot relevant flags and messages with respect to the scenario

Project 2

Highlights

In this project students will integrate the various features together to develop an integrated automated driving model. This model will include previously discussed highway assist (ACC + LCA) + Auto lane change + Predictive speed assist + Intelligent speed assist. Students will then go ahead and test different scenarios that cover all the control functionalities for every feature and provide plots to show the working of the model.
students will develop a new feature model for a Minimum risk maneuver as covered in week 11. The flow chart & pseudocode for the MRM will be provided. Students would first have to implement a function and perform unit testing on the model to show all the states are working. Finally, a scenario for MRM will be given where the student would have to integrate the MRM block.

Key Highlights:

  • Complete lateral and longitudinal feature design implementation
  • Feature model integration using matlab and simulink
  • Unit testing for functional blocks
  • New feature design requirements and implementation
  • Complete simulation for lateral , longitudinal and MRM features.

Deliverables:

  • Model implementation and executable
  • Show suitable plots and simulation results for functional testing
  • Show results for defined scenarios
  • Flow chart for new feature implementation and simulink implementation
  • Feature simulation


WHO IS THIS COURSE FOR ?


  • Freshers, College Students & Working Professionals in Mechanical, Electrical Department can take up this course.
  • Working Professionals, especially mid-career employees who are looking for a job change can take up this course & gain knowledge from this, which can help them in switching careers.
  • Students who are interested to pursue a Master’s Degree

SOFTWARE COVERED


Certification

  • Top 5% of the class will get a merit certificate
  • Course completion certificates will be provided to all students
  • Build a professional portfolio
  • Automatically link your technical projects
  • E-verified profile that can be shared on LinkedIn

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Frequently Asked Questions

1Who can take this course?

This course is designed to give students a hands on approach to learn and develop model based control strategies for driver assist systems. Any student in a Mechanical engineering, electrical and electronics engineering or computer engineering curriculum can take this course. Anyone with an experience in automotive industry or a desire to work in automotive controls domain can also take this course.  Basic knowledge of automotive systems, linear algebra and MATLAB/ Simulink experience would be helpful to successfully complete this course.

2What is included in this course?

This course will go over various control strategies used to develop automotive systems. The course will give students hands on experience in developing algorithms and interfaces for driver assist technologies. At the end of the course, student will be able to build their own model for level 2 autonomous vehicle features & will be able to add their own modifications to improve system capabilities.

3What will the student gain from this course?

  • Student will have a complete hands-on based experience to develop an ADAS system from Level 1 automation to Level 3.
  • Use different tools in matlab , Simulink environment.
  • Experience model based approach in system engineering.
  • Experience different control strategies within the project ex: LQR, Rule based, PID, state-flow.

4What software skills are you teaching and how well are these tools used in the industry?

Front End Technical Design, Development, Cross Platform Testing strategies and industry best practices are being taught. This methodology is the same as used in the industry to develop Web Applications Front End.

5Which companies use these techniques and for what?

 Automotive OEMs like Tesla, GM, Ford, Suppliers like Continental, Delphi/Aptiv , Valeo. Consultant companies like TCS, KPIT, EASI.

6What is the real world application for the tools and techniques will you teach in this course?

  • Model based system engineering
  • Matlab & Simulink environment for ADAS
  • Common control strategies used in Automotive systems
  • Basic programming experience and algorithm development

7How is this course going to help me get a job?

Every company looks for more hands on experience on tools and systems so that the candidates can easily be trained for their jobs. With this course, the candidate will be ready to take on projects and easily adapt to the projects & start working on them.

8How is your course going to help me in my path to MS or PhD?

Through this course, students will be familiar with common control strategies and will obtain hands on experience in simulations and model in loop implementations. This experience can help with the foundation for MS research or Phd work. Using MATLAB and Simulink tools will be very handy to be successful in MS courses offered in the graduate universities.


Flexible Course Fees

Choose the plan that’s right for you

Basic

2 Months Access

$94.99

Per month for 3 months

  • Access Duration : 2 Months
  • Mode of Delivery : Online
  • Project Portfolio : Available
  • Certification : Available
  • Email Support : Available
  • Forum Support : Available
Premium

Lifetime Access

$203.55

Per month for 3 months

  • Access Duration : Lifetime
  • Mode of Delivery : Online
  • Project Portfolio : Available
  • Certification : Available
  • Individual Video Support : 12/ Month
  • Group Video Support : 12/ Month
  • Email Support : Available
  • Forum Support : Available
  • Telephone Support : Available
  • Dedicated Support Engineer : Available

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