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Post Graduate Program in Autonomous Vehicles
Get in-depth insights about the present and future of autonomous vehicles with Skill Lync’s Master’s Certification Program in Autonomous Vehicles.
Beginner
Online
32 Weeks
Quick facts
particular | details | |
---|---|---|
Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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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.
The highlights
- Demo session available
- Multiple enrolment options
- Career support
- Email support
- Forum support
- 100% online
- Completion and Merit certificate
Program offerings
- Multiple enrolment options
- Demo session available
- Career support
- Email support
- Forum support
- 100% online
- Completion and merit certificate
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
certificate providing authority
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
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
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.
The syllabus
Course 1: Applying CV for Autonomous Vehicles using Python
Week - 01: Introduction to Computer Vision
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
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.
FAQs
Yes, you will get a dedicated support engineer with the premium option.
You’ll get add-on industry projects with pro and premium memberships.
Yes, the Autonomous Vehicles programme is completely online. However, you do get access to the offline skill-centre with the premium membership.
You get 12 and 18 months of access with the basic and pro membership, respectively. The premium membership comes with lifetime access.
Yes, a 0% EMI option is available
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