Post Graduate Program in Autonomous Vehicles by Skill Lync: Fee, Duration, How to Apply

Quick Facts

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

Course overview

The Autonomous Vehicles online course by Skill Lync covers a step-by-step syllabus to help you understand the complete process of building an autonomous vehicle. The programme is divided into 4 modules, each of which has various projects that will help you apply all the theoretical concepts covered in the module.

The Autonomous Vehicles course syllabus covers autonomous vehicle controls, path planning, trajectory optimisation, computer vision, mapping, and localisation. Upon finishing the course, you’ll receive a completion certificate, which you can attach to your resume/CV/portfolio/LinkedIn. Additionally, if you manage a place in the top 5 of your batch, you’ll receive a merit certificate as well.

Moreover, with the Autonomous Vehicles training course, you also get dedicated career support. The services include career counselling, resume creation, tool test training, mock interviews, and LinkedIn profile creation. By the end of the course, you’ll have enough skills and credentials to land a job in the up-and-coming autonomous vehicles industry.

Course and certificate fees

You need to pay the Autonomous Vehicles course fee to participate in the course.
You have three enrolment options: Basic, Pro, and Premium. You get different benefits depending on your choice.
You can pay the fee using UPI, credit cards, and debit cards.

Master’s Certification Program in Autonomous Vehicles fee structure

Enrolment option	Fees in INR
Basic – 12 months access	Rs. 25000/month for 10 months
Pro – 18 months access	Rs. 30000/month for 10 months
Premium – Lifetime access	Rs. 35000/month for 10 months

certificate availability

Yes

certificate providing authority

Skill Lync

Eligibility criteria

You need to have an introductory-level proficiency in coding if you wish to make the most of the Autonomous Vehicles certification course.

Certificate qualifying details

To get the completion certificate, you must finish the course along with all the assessments and projects. You’ll receive a merit certificate if you’re in the top 5 of your batch.

What you will learn

Mechanical knowledge Automation skills Knowledge of engineering

Upon completing the Autonomous Vehicles course syllabus, you will be able to:

Apply computer vision
Use software such as Tensorflow, Python, OpenCV, etc.
Develop and implement algorithms
Identify locations on the map with Grid Mapping
Develop 3D models with Lidar and Camera data fusion
Generate estimates using unknown variables when on road
Build control system using Simulink
Build a level-2 adaptive cruise control project
Develop an ADAS system from Level 1 to Level 3
Implement learned concepts using C++, ROS, and Python
Develop software for robots

The syllabus

Course 1: Applying CV for Autonomous Vehicles using Python

Week - 01: Introduction to Computer Vision

Introduction to Autonomous Vehicles
Introduction to Computer Vision
Applications of Computer Vision
Course Content - Introduction
Understanding Images

Week - 02: Image Processing Techniques – I

Image Filters
Correlation
Convolution
Noise in Images
Types of Noise
Filters for Noise
Image Gradients
Edge Detection Techniques

Week - 03: Image Processing using Edge and Line Detection

Canny Edge Detector
Hough Transformation - Lines
Hough Transformation - Circles
Domain Transformation
Understanding Frequency Domain
Spatial to Frequency Domain transformations

Week - 04: Projective and Stereo Geometry

Image coordinate systems
Projective Geometry
Perspective Projections
Multiview Geometry
Stereography and Depth Imaging
Stereo Correspondence

Week - 05: 3D Computer Vision

Projective Geometry for 3D
Camera calibration methods
Epipolar Geometry
Stereovision

Week - 06: Feature Extraction , Neural Networks and Image Classification

Image Classification
Dimensionality Reduction
Principal Component Analysis
Convolutional Neural Networks
Datasets
Mobile Net Architecture
Image Classification - Performance Metrics

Week - 07: Feature Detectors and Descriptors

Feature Detectors
- Moravec’s Detector
- Harris Corner Detector
Feature Descriptors
- SIFT
- ORB
Feature matching methods

Week - 08: Optical Flow

Optical Flow
Horn-Shunck method
Lucas Kanade sparse optical flow
Gunnar-Farneback Optical flow
Deep learning based optical flow models

Week - 09: Object Tracking

Introduction to Object Tracking
Deep SORT
Lucas-Kanade-Tomasi(KLT) Tracker
Minimum Of Sum Squared Error (MOSSE) Tracker
Mean Shift Tracker

Week - 10: Image Segmentation

Introduction to Image Segmentation
Methods of Segmentation
Applications of Segmentation
Thresholding based segmentation
Otsu’s Thresholding
Morphological Operations
Connected Components
Datasets for image segmentation
Deep learning architectures for image segmentation

Week - 11: Object Detection

Introduction to Object Detection
Region Proposals
Graph cut segmentation
Selective search
Object Detection Datasets
Object Detection Models
Tensorflow model zoo
Evaluation metrics for object detection models

Week - 12: 3D Object Detection

Introduction to 3D Object Detection
Types of 3D Object Detection
Stereo Image based Detection
Monocular 3D Object Detection

Course 2: Localization, Mapping, and SLAM using Python

Week 1 - Introduction to Localization

Methods of performing localization (With the example of the self-driving car)
Different sensors used for localization (LiDAR, RADAR, GNSS, INS, wheel encoders)
Sensing models for sensors used in practice that increase the presence of uncertain results in imperfect models
Downloading Python and Jupyter notebook
NumPy and matplotlib

Week 2 - Probability Theory Refresher and Probabilistic Modelling

Error sources: limitations of sensing - deterministic vs. non-deterministic
Augmented odometry model with error
Deviations because of error additions
Error propagation in IMU
Concepts such as random variable, random vectors, density function, joint density, marginal density, conditional independence
Probability Density Function (PDF), gaussian, multivariate gaussian
Variance and covariance
Random variables in robotics state estimation
Probability distribution

Week 3 - Bayesian Filtering

Probabilistic generative laws
Definitions for state and environment
Concepts of belief, posterior
Probabilistic models for perception and state transition
Bayesian filter, Markov assumption
Kalman filters and Particle filters: Introduction

Week 4 - Kalman Filter

Kalman filter and its derivation from Bayes filter
Kalman Gain (with an example)
Kalman filter assumptions and optimality

Week 5 - Extended Kalman Filter and Unscented Kalman Filter

Limitations of Kalman filter
Variants that overcome the limitations
Jacobians in Extended Kalman filter

Week 6 - Particle Filter (aka Monte Carlo Localization)

Map-based localization
Derivation of particle filter from the Bayes filter
PF handling non-linearity
Properties of particle filter

Week 7 - Multi Sensor Fusion

Loosely coupled and tightly coupled techniques in Extended Kalman filtering
Extension of state vector to accept multiple inputs

Week 8 - Mapping

Map generation process from the perspective of self-driving car

Week 9 - Introduction to SLAM with EKF SLAM

Explanation of how SLAM problems resemble chicken and egg proble
Explanation of simplest SLAM implementation - EFF, SLAM, and loop closure

Week 10 - Graph SLAM

Difference between offline SLAM and online SLAM
Motivate graph based modeling of SLAM problem
Explanation of mathematical formulation of Graph SLAM
Explanation of how Graph SLAM can be used offline to generate a map of environment

Week 11 - FastSLAM

Particle filter based SLAM
Mathematical derivation and comparison with three SLAM techniques

Week 12 - Other Implementations for SLAM and ROS Intro

Explanation of factor graph and pose graph formulations of SLAM problem
Explanation of how camera is used as sensing source in SLAM
Example of factor graph implementation on drone fitted with downward facing camera
ROS - what it is and important concepts (Publisher, subscriber, topics, message)

Course 3: 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

Course 4: 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

Admission details

Visit the course webpage: https://skill-lync.com/mechanical-engineering-courses/masters-certification-program-autonomous-driving/about#pricing
Scroll down to the Fee section.
Choose a course option according to your preference.
Click on the “Enroll Now” button located beneath your preferred course option.
Fill and submit the short form.
Pay the fee using your preferred transaction option.

Filling the form

When you click on the “Enroll Now” option, a short form will pop up. Provide your name, email ID, and contact number for this form. After submitting, you’ll be prompted to make the payment. Once that’s done, you’ll be enrolled in the Autonomous Vehicles course.

How it helps

The Autonomous Vehicles training is designed with inputs from industry experts and academicians to ensure that you get the skills expected in the industry. Throughout the course, you will be supported by Skill-Lync’s team of support engineers, who’ll always be available to answer all your questions.

Moreover, after you have completed 80% of the course, Skill-Lync’s career success team will start training you for your interviews. They’ll also help you apply for the job that you are best equipped for. On top of that, they will also help you enhance your resume and your LinkedIn profile.