Does Skill Lync provide demo sessions for the course?

Yes, you can request a demo here: https://skill-lync.com/computer-science-engineering-courses/masters-certification-program-motion-planning-trajectory-generation/about#pricing.

How many courses does this master’s Motion Planning and Trajectory Generation (ADAS) programme include?

There are a total of five courses.

Does Skill Lync provide internships for the Master's Certification Program in Motion Planning and Trajectory Generation (ADAS)?

Yes, you can get a 3-months paid internship with the Premium course plan.

Post Graduate Program in Motion Planning and Trajectory Generation (ADAS) by Skill Lync: Fee, Duration, How to Apply

Post Graduate Program in Motion Planning and Trajectory Generation (ADAS)

Skill Lync

Join the course to learn everything about motion planning and trajectory generation in autonomous vehicles.

Online

32 Weeks

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

particular

details

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

Course 1: Core and Advanced Python Programming

Week 1 - Introduction to Python, Python Basics

Features and uses of Python
Program execution
Installation of IDE
Identifiers and keywords
Types of comments
Data types
Variables
Arithmetic operators
Assignment operators
Input and print statements

Week 2 - Strings, Decision Control Statements

Definition of string
Operations accessing string elements
Relational operators
Logical operators
Conditional expressions
If, If..else, If..elif

Week 3 - Repetition Statements and Console Input-Output

Use of while and for
Break and continue
Pass and else statements
Formatted input and output

Week 4 - Lists, Tuples, Sets, Dictionary

Use of while and for
Break and continue
Pass and else statements
Formatted input and output

Week 5 - Functions and Recursion, Functional Programming and Lambda Functions

Defining a function
Types of arguments
Global and local variables
Functions as arguments
Implementing Lambda functions
Map, Reduce, and Filter functions

Week 6 - File Input-Output and Modules

Read-write operations
With the keyword
File opening modes
Moving within a file
Serialization
File and directory operations
Importing a module
Variations of import
Third-party packages

Week 7 - Classes and Objects

Class variables
Methods
Operator overloading
Reuse
Containership
Inheritance

Week 8 - Exception Handling, Iterators and Generators

Iterables and iterators
Syntax errors and exceptions for:
try-except
else
finally blocks

Week 9 - Data Analysis with Pandas

Installing Pandas
Loading files
CSV files
JSON files
Dataframes

Week 10 - Numeric and Scientific Computing using NumPy

NumPy: Introduction
OpenCV
Images and NumPy Arrays

Week 11 - Graphical User Interfaces with Tkinter

Introduction to Tkinter
Setting up a GUI with widgets
Connecting GUI widgets with callback functions

Week 12 - Interacting with Databases

SQLite: Introduction
Connecting and inserting data to SQLite via Python
Selecting, deleting, and updating SQLite records

Course 2: Autonomous Vehicle Controls using MATLAB and Simulink

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

Course 3: Introduction to ROS/GitHub/Linux

Week 1 - Introduction to Autonomous Vehicles

Introduction to Self-driving Vehicles
Levels of Autonomy
What is ROS?
Why is ROS important for auto vehicles?
Integration of ROS with Self-driving Vehicles

Week 2 - Introduction to Linux System

Introduction to Linux
Difference between Windows and Linux
Distributions in Linux
Install dual OS on Windows Laptop - Install Ubuntu
Explain the working of Ubuntu
Introduction to Shell Commands
Introduction to Git

Week 3 - ROS Introduction

ROS Architecture and Philosophy
How to install ROS?
Abstractions in ROS
Applications of ROS

Week 4 - Programming Basics for Robotics

Why python for robotics?
Python/C++ Essentials
- How to create variables?
- Different Operators
- Conditional Statements and Loops
  - If, else, and elif
  - For loop
  - While loop
- Functions
- Lists, tuples, and dictionary
- OOPs (object-oriented programming)

Week 5 - ROS File System Concepts

ROS Filesystem Level
- Navigating in ROS (catkin, rosbuild)
- Creating Packages
- Building Packages (catkin_make)
- Package Dependencies

Week 6 - ROS Computation Graph Concepts -I

ROS Graph Concepts
Nodes
- Master Node
- Slave Node
rostopic
- What is a topic in ROS?
- rostopic list/echo
- Messages and message structure
- Services(using rosservice) and Parameters(using rosparam)
- Bags (rosbags, rqt, rosplay, and rosrecord)

Week 7 - ROS Computation Concepts and Visualization

Writing Publisher Node and Subscriber Node
Working with Publishers and Subscribers
2D Visualization
- Plotting Message Data using rqt_plot
- Real-time Plotting using plotJuggler
3D Visualization
- rViz
- Gazebo simulator

Week 8 - ROS for Autonomous Vehicles

Sensors
- Different Types of Sensors
- Sensory Data
- Visualization of Sensory Data in Rviz
Dataset available - KITTI, nu scene, COCO, etc
Introduction to the autonomous driving simulator: CARLA
- Installation of CARLA
Integration of ROS and CARLA
- ROS bridge installation

Week 9 - Mobile Robotics: Basics

Wheeled Vs Legged Robots
Differential Drive Robot Basics
Kinematic Bicycle Model

Week 10 - Mobile Robotics: Perception

Introduction to Computer Vision
OpenCV in Python
Vision-based Sensors
Feature Extraction
Object Detection and Tracking

Week 11 - Mobile Robotics: Localization and Mapping

Need for Localization for Autonomous Driving
SLAM
Grid Maps
Sensors - Wheel Encoders and IMU
Accumulation of Errors
Kalman Filter and Uncertainty

Week 12 - Mobile Robotics: Path Planning

Vision-based Navigation
Path Execution and Obstacle Avoidance
Static and Dynamic Obstacles
Understanding of Cost and Grid Maps
Path Planning Algorithms
- RRT
- A*

Course 4: Numerical Optimization

Week 01 - Strategies for optimization problem formulations (modeling)

Mathematical Formulation
Example: A Transportation Problem
Continuous versus Discrete Optimization
Constrained and Unconstrained Optimization
Global and Local Optimization
Stochastic and Deterministic
Optimization Convexity

Week 02 - Unconstrained Optimization

Unconstrained minimization problems
Gradient descent method
Steepest descent method
Newton’s method

Week 03 - Conjugate Gradient Methods

Linear Conjugate Gradient Method - Properties & rate of convergence
Conjugate Direction Method - Properties & rate of convergence
Nonlinear Conjugate Gradient Methods - Properties & rate of convergence
Fletcher–Reeves Method
Polak–Ribiere Method
Variants Global convergence and numerical performance

Week 04 - Quasi-Newton methods

The BFGS Method
- Properties
- Global Convergence of the BFGS Method
- Superlinear Convergence of the BFGS Method
- Implementation
Limited-Memory Quasi-Newton Methods
Limited-Memory BFGS Method

Week 05 - Non-Gradient Optimization

Pattern search method - Stochastic methods
- Single-stage stochastic optimization
- Multistage stochastic optimization
Markov decision problems

Week 06 - Least-Squares Problem

Linear Least-Square problems and nonlinear least-square problems
Gauss-Newton method
- Convergence
Levenberg–Marquardt method
- Convergence and implementation

Week 07 - Constrained Optimization

Local and Global Solutions
A Single Equality Constraint
A Single Inequality Constraint
Two Inequality Constraints
First-Order Optimality Conditions
Second-Order Conditions
A geometric viewpoint
Lagrange multiplier method

Week 08 - Duality

The Lagrange dual function
The Lagrange dual problem
Geometric interpretation
Saddle-point interpretation
Optimality conditions

Week 09 - Lagrange Multiplier Algorithms

The Quadratic penalty method
Non-smooth penalty functions and l1 penalty method
Augmented Lagrangian method
ADMM algorithm and convergence properties

Week 10 - Convex Programming

Linear optimization problems
Quadratic optimization problems
Geometric programming

Week 11 - Dynamic Optimization Technique (dynamic programming)

Formalizing the dynamic-programming approach
Dynamic Optimization using Analytic and Evolutionary Approaches
Numerical experiment with a varied dimensional problem
Optimal capacity expansion
Continuous state-space problems
Reading assignments

Course 5: Path Planning & Trajectory Optimization Using C++ & ROS

Week 1- Introduction

Graph-Based Algorithms
Breadth-First Search Algorithm
Depth-First Search Algorithm

Week 2- Configuring Space for Motion Planning

How to Use the Configuration Space?
Representing Configuration Space as a Graph
Planning using Visibility Graph
Finding the Shortest Path.
Dijkstra’s Algorithm, A*, Bellman-Ford Algorithm

Week 3- Random Sampling-Based Motion Planning

Various Types of Rapidly Exploring Random Tree(RRT)
Application of RRTs
Path Planning using the RRT Algorithm
Setting up the Ubuntu Environment

Week 4- Robot Operating System

Setting up ROS
Following Instructions on the ROS Website
Adding ROS to the Docker Container
Introduction to Cmake
Programming using ROS
Introduction to 3-D Visualization Tool - Rviz
Difference between
- ROS/RTOS
- ROS1/ROS2
DDS
Middleware

Week 5- Motion Planning with Non-Holonomic Robots

Path and Speed Planning
Trajectory Representations
- Splines
- Clothoid
- Bezier Curves
- Polynomials
Introduction to Frenet Frame
Planning in Frenet Frame
Boundary Value Constraint Problem and Methods
Pointwise Constraint Problem and Methods

Week 6- Mobile Robot Collision Detection

Collision Detection for Static Obstacles
Motion Prediction for Dynamic Obstacles
Motion Prediction in Frenet Frame with Kalman Filters
Collision Prediction for Dynamic Obstacles

Week 7- Hierarchical Planning for Autonomous Robots

Route Planning, A*, D*, D* lite
HD Maps, SD Maps
Behavior Planning - State Machines, Decision Tree, Behavior Tree, etc.
Behavior and Motion Planning Integration

Week 8- Trajectory Planning

Polynomial Planners
Motion Planning with Differential Constraints
Lattice Planners
Collision Checking
Trajectory Selection (Cost Functions)

Week 9- Planning Algorithm

Vehicle and Tire Model
Optimal Control
MPC Planners

Week 10- Planning in Unstructured Environments

Unstructured Planner: Hybrid A*
Parking Planner
Automated Driving Open Research (ADORe)

Week 11- Reinforcement Learning for Planning

Machine Learning
Markov Decision Process
Policy Evaluation
Value iteration
Reinforcement Learning
On/Off Policy, Model-based/Model-free Monte Carlo
Bellman Optimality, SARSA
Q-learning, Epsilon Greedy
Decision Making for AVs

Week 12- Conclusion

Overview of the Topics Learned
Paper Review
Non-Traditional Applications

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