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Introduction to Advanced Driver Assistance System using MATLAB & Simulink

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Domain : Electrical & Electronics, Mechanical, Automotive, Instrumentation & Mechatronics

Pre-requisites : Basic Insights on Model Based Development (MBD) & Advanced Driver Assistance System

Introduction

A Quick Overview

MATLAB Model-Based Development (MBD) is an Embedded Software Development Technology, where a model is used to verify the design requirements & that model is realized as a code which finally runs on target hardware. MBD can be implemented across all systems in automotive software development platforms, one such being the Advanced Driver Assistance Systems (ADAS). ADAS is an electronic system which helps the driver to ease the process of driving. 

Learning this course would enhance your knowledge on some of most sought - after concepts in automotive embedded software development:
  1. Model-Based Development
  2. Model in Loop Testing (MIL)
  3. Software in Loop (SIL) Testing
  4. Auto Code Generation
  5. Development of an ADAS feature from scratch. 
Most of the leading automotive firms are constantly on the look-out for engineers with strong MATLAB / Simulink knowledge along with a good knowledge in MBD & MIL - SIL testing.


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COURSE SYLLABUS

Fundamentals & Basics of MATLAB scripting is covered in this section. If you have no prior MATLAB scripting knowledge, you can learn the fundamentals of it from this course. Topics ranging from “Introduction to using the MATLAB software” to “programmatically controlling a Simulink model” are discussed here.


Here is a detailed list of the key concepts that you will learn from this course.


  • Introduction to MATLAB scripting and Syntax Basics
  • Vectors, Matrices & Data Types in m-scripting
  • MATLAB Operators, Decision Making Statements & Strings in m-scripting
  • MATLAB Data Structures Overview & Cell Arrays in m-scripting
  • Tables & Structures, MATLAB Functions & Callbacks in m-scripting
  • File Handling Formats, Debugging & Flow Control Logics in m-scripting
  • Block properties, Different Workspaces in m-scripting
  • Programmatically accessing Simulink in m-scripting

Fundamentals & Basics of MATLAB Simulink is discussed in this section. If you do not have prior MATLAB or Simulink knowledge, you can learn the basic fundamentals from this course.

Topics range from “Opening a Blank Simulink Model to Adding Blocks from Library to Programmatically Change  Block Properties & its Fundamentals” are discussed here.


  • Introduction to Simulink & Simulink Toolbars
  • Block Settings, Model Annotation, Simulink Solvers
  • Sources & Sink Libraries
  • Math Operations; Logical & Bit Operations
  • Ports & Subsystems; Atomic Subsystems
  • Masked Subsystem & Linked Libraries
  • Continuous, Discontinuous & Discrete Blocks
  • User-Defined Functions & Lookup Tables
  • Mathematical Model Representation & System Models Creation in Simulink
  • Stateflow-1
  • Stateflow-2

In this section, an overview of Automotive Software Industry, the size at which it has grown & its demands in today’s market are discussed briefly. Need for Model Based Development, key concepts in developing a complete MATLAB model from scratch & challenges to be addressed by an MBD engineer are discussed here.


  • Overview of Automotive Industry
  • Software Development demands of Automotive Industry
  • Model Based Development in Automotive Industry & Model Based Development in MATLAB
  • Requirement Analysis in Model Based Development
  • Model Based Development Configuration Parameters Settings
  • Creating Simulink Data Dictionary
  • Accessing Simulink Data Dictionary & Port Property Settings
  • Signal Names & Signal Property Configuration

In this section, the validation & code generation process for the developed model is discussed. Simulating a model, generating C - code from it & validating both the model & code using concepts like Model in Loop (MIL) & Software in Loop (SIL) are discussed here.

  • Model Simulation & Model Advisor Report
  • Code Generation Settings, Auto-code Generation
  • Overview of Model in Loop, Software in Loop & Hardware in Loop
  • Testing Theory
  • Test Report Analysis (Coverage Analysis & Different Techniques)
  • Model in Loop Testing
  • Software in Loop Testing
  • Overview of Hardware in Loop Testing

In this section, an Introduction to Advanced Driver Assistance System (ADAS), levels of autonomous driving is discussed. Apart from these, basic level software architecture for some of the features like Traffic Sign Recognition, Adaptive Cruise Control, Anti-Lock Braking System are also discussed. Moreover, you will focus on an ADAS feature - Tilt & Telescopic Switch (this feature is implemented by OEMs like Toyota & Honda) & its Control Model is developed from the beginning using MATLAB and Simulink using MBD concepts.

  • Introduction to ADAS & Levels of Autonomous Driving
  • Overview to ADAS Features - 1
  • Overview to ADAS Features - 2
  • ADAS Project - Tilt & Telescopic Steering Column
  • Requirement Analysis & Problem Understanding
  • MATLAB Model Development of Tilt & Telescopic Function Feature

Projects Overview

Mini Project

General Overview:

Identifying the direction of the vehicle is one of the important & diverse features in Autonomous driving & Advanced Driver Assistance Features. This particular sub-feature of identifying the direction of vehicle is basically identifying the direction the vehicle is taking based on the camera input.
Camera reads the road signs & stores into its memory with unique values for left turn, right turn & straight drive. Depending on the direction it is taking, final indication is given to the driver – as an indication if he is driving in the recommended direction or not.
Vehicle Direction Determination can also be coupled along - side features like GPS systems to identify whether the vehicle is reaching its destination in an optimized manner. This sub feature can also be used along with Lane Detection, Highway Warning, Ramp Entry / Exit in Wrong Way Detection etc.

Objective of Mini Project:

1. Development of MATLAB Simulink model as per requirement.
2. Tag the requirements to the simulink model; tagging requirement 1 & requirement 2 to their corresponding subsystems is fine.
3. MBD compliant changes, Data Dictionary creation & code generation is added advantage, and that is not scope of this project.
4. If choosing code generation, Storage class for Input signals: ImportedExtern; Storage class for Output signal: Export to File; Storage class for local signals: localizable; Storage class for calibration signals: Const.
5. Choose sample time for all signals as 0.01s


Requirement - 1:
• Steering wheel input as yaw rate (Signal name: SteeringWheel_YawDegreeInput) is the input for this system.
• This is compared against 3 angular values, one each for left turn, right turn & straight drive (Calibration Values: Right_Turn_AngularLimit, Left_Turn_AngularLimit, Straight_Drive_Steering_Angle) to say which specific direction the steering wheel is turning towards.
• Use switch blocks to compare & develop this requirement. Keep this requirement in a subsystem & output of this requirement is a local signal (Signal Name: Vehicle_Turn_Status).


Requirement – 2:
• Keep this requirement as a separate subsystem. Inputs to this requirement are local signal from requirement 1 (Signal Name: Vehicle_Turn_Status) & an input signal from camera (Signal Name: CameraInput_RoadSign), which confirms the occurrence of a road sign.
• Signal Vehicle_Turn_Signal is compared against calibration values (Calibration Values: RightTurn_RoadSign, LeftTurn_RoadSign, Straight_RoadSign), if each of them is found equal, then each of the three corresponding output is compared against the camera input signal, CameraInput_RoadSign.
• Using a logical operator block, only one among them is finally given as output signal (Signal Name: Vehicle_Direction_Indicator).

Signals & Calibration Data List:

Main Project

General Overview:
Adaptive Cruise Control Feature for passenger cars allows the host vehicle to adapt to the speed in line with the flow of traffic. Driving in heavy traffic or keeping a safe distance to the preceding vehicle calls for a high level of concentration. The Adaptive Cruise Control feature can reduce the stress on the driver by automatically controlling the vehicle speed & maintaining a predefined minimum distance to the preceding vehicle. As a consequence, the driver enjoys more comfort & can concentrate on the road little better.
A radar sensor is usually at the core of the Adaptive Cruise Control. Installed at the front of the vehicle, the system permanently monitors the road ahead. As long as the road ahead is clear, cruise control feature maintains the speed set by the driver. If the system spots a slower vehicle within its detection range, it gently reduces speed by releasing the accelerator or actively engaging the brake control system. If the vehicle ahead speeds up or changes lanes, the cruise control automatically accelerates to the driver’s desired speed.
Standard Adaptive Cruise Control can be activated from speeds of around 30 km/h (20 mph) upwards and supports the driver, primarily on cross-country journeys or on freeways. The cruise control stop & go variant is also active at speeds below 30 km/h (20 mph). It can maintain the set distance to the preceding vehicle even at very low speeds and can decelerate to a complete standstill. When the vehicle remains stopped longer, the driver needs only to reactivate the system, for example by briefly stepping on the gas pedal to return to cruise control mode. In this way, cruise control stop & go supports the driver even in heavy traffic and traffic jams.
Since Adaptive Cruise Control is a comfort and convenience system, brake interventions and vehicle acceleration only take place within defined limits. Even with Adaptive Cruise Control switched on, it remains the driver’s responsibility to monitor the speed and distance from the vehicle in front.

Objective of Main Project:
1. Developing Adaptive Cruise Control feature as per the Requirement Document using MATLAB Simulink.
2. Follow all the MBD related processes: Requirement Tagging & Traceability, SLDD creation, Configuration Parameter changes, Model Advisor check & Code Generation.
3. In Configuration Parameters: enable “Support Floating Numbers” under Code Generation settings.
4. Use Embedded Coder to generate the code.
5. If choosing code generation, Storage class for Input signals: ImportedExtern; Storage class for Output signal: Export to File; Storage class for local signals: localizable; Storage class for calibration signals: Const.
6. Choose sample time for all signals as 0.01s

Requirement 1– Lead Vehicle:
• Lead Vehicle is a vehicle which is driving in the road ahead of our drive vehicle. Two input signals (Signal Name: CameraInput_LeadVehicle & RadarInput_LeadVehicle).
• Ideally sensor fusion techniques will be deployed to process & analyze data from camera & radar. For complexity reasons, let’s not adapt to any such algorithms.
• We can simply add both the radar & camera inputs & the corresponding output is read as Speed profile output (Signal Name: LeadVehicle_Speed).
• Speed data of the lead vehicle is critical in implementing the Adaptive Cruise Control algorithm.

Requirement 2 – Drive Vehicle:
• Drive Vehicle is the vehicle driven by the user & this is the vehicle which has ACC algorithm in it.
• Like the Lead Vehicle, Drive Vehicle algorithm also has 2 input signals (Signal Name: CameraInput_DriveVehicle, RadarInput_DriveVehicle) & one signal coming as an Input to this subsystem (Signal Name: Acceleration_Mode) – three inputs into this requirement in total.
• Like the above requirement, sensor fusion techniques will also be deployed here, for complexity reasons we are ignoring them.
• Two output signals come from this subsystem (Signal Name: DriveVehicle_Speed & LeadVehicle_Detected).
• Signal DriveVehicle_Speed is summation of three input signals mentioned above & LeadVehicle_Detected is renamed from Input Signal RadarInput_DriveVehicle by mere use of Signal Conversion block.

Requirement 3 – Adaptive Cruise Control Algorithm:
• Adaptive Cruise Control feature has 3 major modes of operation: OFF Mode, STANDBY Mode & ON Mode. This particular requirement has to be implemented as state machine logic in Simulink.
• The input signals to this state machine system are (Signal Name: Time_Gap, Set_Speed, Set_Gap, CruiseSwitch, SetSwitch).
• Also, the output signals (Signal Name: DriveVehicle_Speed & LeadVehicle_Detected) from requirement-2 is fed back as an input signal into this state machine block.
• Additionally, output signal (Signal Name: LeadVehicle_Speed) from requirement-1 is given as an input signal to this state machine block as well.
• Output from this subsystem is a signal (Signal Name: Acceleration_Mode) which governs the vehicular speed of the drive vehicle which automatically adjusts its speed & velocity to match the lead vehicle.

Requirement 3a – ACC OFF MODE state logic:
• This is the default state inside state machine logic. Output signal Acceleration_Mode is at value 0 in this state.
• This state is governed by input signal CruiseSwitch.
• If CruiseSwitch is equal to 1, state ACC STANDBY mode will get activated. If CruiseSwitch is equal to 0, state ACC OFF mode will get activated, from either ACC ON mode or ACC STANDBY mode states.

Requirement 3b – ACC STANDBY MODE state logic:
• This is the second activated state inside state machine logic. Output signal Acceleration_Mode is at value 1 in this state.
• This state is governed by both input signals CruiseSwitch & SetSwitch.
• If CruiseSwitch is equal to 1, state ACC STANDBY mode will get activated. If CruiseSwitch is equal to 0, state ACC OFF mode will get activated, from either ACC ON mode or ACC STANDBY mode states.
• If SetSwitch is equal to 1, state ACC ON mode will get activated. If SetSwitch is equal to 0, state ACC STANDBY mode will get activated.

Requirement 3c – ACC ON MODE state logic:
This state will be activated when input signal SetSwitch is equal to 1. There are 6 sub states to this state logic: They are:
• LeadVehicle_Detected_Follow (Default)
• LeadVehicle_Not_Detected
• LeadVehicle_Detected_Resume
• LeadVehicle_Not_Detected_Resume
• LeadVehicle_Speed_lessthan_Set_Speed
• LeadVehicle_Speed_equal_Set_Speed.

Requirement 3c (i) – LeadVehicle_Detected_Follow (ACC ON MODE):
• This is the default sub state inside ACC ON MODE state. Output signal Acceleration_Mode is equal to 2.
• Condition to transit from LeadVehicle_Detected_Follow to LeadVehicle_Not_Detected; Input signal condition LeadVehicle_Detected == 0.
• Condition to transit from LeadVehicle_Detected_Follow to LeadVehicle_Speed_lessthan_Set_Speed; Input Signals condition (LeadVehicle_Detected == 1) && (LeadVehicle_Speed < Set_Speed) || (Time_Gap < Set_Gap).

Requirement 3c (ii) – LeadVehicle_Not_Detected (ACC ON MODE):
• Output signal Acceleration_Mode is equal to 1.
• Condition to transit from LeadVehicle_Not_Detected to LeadVehicle_Detected_Follow; Input signals condition [(LeadVehicle_Detected==1) && (DriveVehicle_Speed == Set_Speed) && (LeadVehicle_Speed >= Set_Speed) && (Time_Gap >= Set_Gap)]
• Condition to transit from LeadVehicle_Not_Detected to LeadVehicle_Speed_lessthan_Set_Speed; Input signals condition [(LeadVehicle_Detected == 1) && (LeadVehicle_Speed < Set_Speed) || (Time_Gap < Set_Gap)]

Requirement 3c (iii) – LeadVehicle_Detected_Resume (ACC ON MODE):
• Output signal Acceleration_Mode is equal to 3.
• Condition to transit from LeadVehicle_Detected_Resume to LeadVehicle_Detected_Follow; Input signals condition [(DriveVehicle_Speed == Set_Speed) && (LeadVehicle_Speed >= Set_Speed) && (Time_Gap >= Set_Gap)]
• Condition to transit from LeadVehicle_Detected_Resume to LeadVehicle_Not_Detected_Resume; Input signal condition LeadVehicle_Detected==0.
• Condition to transit from LeadVehicle_Detected_Resume to LeadVehicle_Speed_equal_Set_Speed; Input Signal condition [(DriveVehicle_Speed < Set_Speed) && (LeadVehicle_Speed > DriveVehicle_Speed) && (Time_Gap >= Set_Gap)]

Requirement 3c (iv) - LeadVehicle_Not_Detected_Resume (ACC ON MODE):
• Output signal Acceleration_Mode is equal to 1.

Requirement 3c (v) - LeadVehicle_Speed_lessthan_Set_Speed (ACC ON MODE):
• Output signal Acceleration_Mode is equal to 4.
• Condition to transit from LeadVehicle_Speed_lessthan_Set_Speed to LeadVehicle_Not_Detected; Input signal condition [(LeadVehicle_Detected == 0) && (DriveVehicle_Speed == Set_Speed)]
• Condition to transit from LeadVehicle_Speed_lessthan_Set_Speed to LeadVehicle_Speed_equal_Set_Speed; Input signals condition [((LeadVehicle_Speed*1.25>=DriveVehicle_Speed) && (LeadVehicle_Speed * 0.75<=DriveVehicle_Speed)) && (DriveVehicle_Speed < Set_Speed) && ((Time_Gap<=1.25*Set_Gap) && (Time_Gap >=0.75*Set_Gap))]

Requirement 3c (vi) - LeadVehicle_Speed_equal_Set_Speed (ACC ON MODE):
• Output signal Acceleration_Mode is equal to 5.
• Condition to transit from LeadVehicle_Speed_equal_Set_Speed to LeadVehicle_Not_Detected_Resume; Input signal conditions is [(LeadVehicle_Detected == 0) || (DriveVehicle_Speed <= Set_Speed)]
• Condition to transit from LeadVehicle_Speed_equal_Set_Speed to LeadVehicle_Detected_Resume; Input signal conditions [(DriveVehicle_Speed < Set_Speed) && (LeadVehicle_Speed > DriveVehicle_Speed) || (Time_Gap >= Set_Gap)]
• Condition to transit from LeadVehicle_Speed_equal_Set_Speed to LeadVehicle_Speed_lessthan_Set_Speed; Input signals conditions [(LeadVehicle_Speed<Set_Speed) && (LeadVehicle_Speed<DriveVehicle_Speed) || (Time_Gap==0.75*Set_Gap)]

Signals & Calibration Data List:

 

WHO IS THIS COURSE FOR ?

  • Freshers, College Students & Working Professionals can take up this course.
  • Working Professionals, especially mid - career employees who are looking for a job change in their organisations can take on this course & gain knowledge from to help them move up the ladder.
  • Almost all the top OEMs work on projects related to MATLAB Model Based Development, MIL - SIL Testing and ADAS related projects. Adding these skills will enhance your portfolio.

SOFTWARE COVERED

MATLAB

MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation.

SIMULINK

Simulink, developed by MathWorks, is a graphical programming environment for modeling, simulating and analyzing multi domain dynamical systems.

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Hands-on experience on development of embedded systems model, their verification & validation on ADAS applications.

You will have an edge over your peers by working extensively on industry-relevant projects, practice on tools and software that will set you apart and help you in getting ahead of the competition. Our course will strengthen your portfolio to get better grants and scholarship opportunities for MS Admits, explore options in Research & Development, and land that much-coveted job in top core companies. 

Advanced Driver Assistance Systems (ADAS) is seen by many automotive companies as the way forward. Automotive giants like Tesla, Renault have started extensive research & development on ADAS. Cars in India are equipped with ADAS features like ABS, Cruise Control already. Indian college curriculum does not cover these topics & how they are incorporated in the Industry.

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