Post Graduate Program in Autonomous Vehicles
4.5 /5- Offered bySkill Lync
Post Graduate Program in Autonomous Vehicles at Skill Lync Overview
Duration | 32 weeks |
Total fee | ₹2.50 Lakh |
Mode of learning | Online |
Credential | Certificate |
Post Graduate Program in Autonomous Vehicles at Skill Lync Highlights
- Earn a certificate of completion from Skill Lync
- Learn directly from best in class 4 Industry Experts
- Easy EMI payment option available
- Project based Learning through 8 Comprehensive projects with dedicated support
- Learn tools like- MATLAB, Python, TensorFlow, OpenCV, Keras TensorFlow 2, C++, ROS, Eclipse ADORe, Simulink
- Prepare for roles like: Computer Vision Engineer, Perception Engineer, Vision Engineer, Software Engineer- Computer Vision, Perception Software Engineer, Function Safety, Manager for ADAS, Motion Planning & Control Engineer.
Post Graduate Program in Autonomous Vehicles at Skill Lync Course details
- Highly suited for beginners
- Ability to rationalize the applications of methodologies
- Hands-on experience in implementing state of the art computer vision algorithm
- Real-world problems and examples give a ready-for-industry learning experience
- Key tools such as Tensor flow, Keras, Python OpenCV, ML & DL approaches will be learned
- This is a comprehensive program on Autonomous Vehicles using AV system design and key algorithms and techniques that are commonly used
- This program focuses right from the basics of image processing techniques to in-depth concepts of 3D vision, computer architecture frameworks such as ResNet, Yolo, etc.
- The course provides a foundation in Linear algebra, Python and OpenCV, and takes a gradual approach to advanced concepts of image segmentation, stereo imaging, etc.
- The students will acquire hands-on experience in implementing the state of the art computer vision algorithms.
Post Graduate Program in Autonomous Vehicles at Skill Lync Curriculum
Course 1 - Applying CV for Autonomous Vehicles using Python
Introduction to Computer Vision
Image Processing Techniques ? I
Image Processing using Edge and Line Detection
Projective and Stereo Geometry
3D Computer Vision
Feature Extraction , Neural Networks and Image Classification
Feature Detectors and Descriptors
Optical Flow
Object Tracking
Image Segmentation
Object Detection
3D Object Detection
Course 2 - Path Planning & Trajectory Optimization Using C++ & ROS
Introduction
Configuring Space for Motion Planning
Random Sampling-Based Motion Planning
Robot Operating System
Motion Planning with Non-Holonomic Robots
Mobile Robot Collision Detection
Hierarchical Planning for Autonomous Robots
Trajectory Planning
Planning Algorithm
Planning in Unstructured Environments
Reinforcement Learning for Planning
Conclusion
Course 3 - Localization, Mapping and SLAM using Python
Introduction to Localization
Probability Theory Refresher
Probabilistic modelling & Bayesian Filtering
Kalman Filter
Extended Kalman Filter and Unscented Kalman Filter
Particle Filter (aka Monte Carlo Localization)
Multi Sensor Fusion
Introduction to Mapping and SLAM
Graph SLAM
FastSLAM
Other Implementations for SLAM
ROS Extra Lecture
Course 4 - Autonomous Vehicle Controls using MATLAB and Simulink
Course Overview and Classical control
Longitudinal Controller Design
Adaptive cruise control model
Advanced ACC - ACC Feature Modification
Lateral Control for Vehicles - Geometric Method
Lateral Controller Model for Vehicles- Dynamic Modeling
Lane Centering Assist
Complete Level 2 Feature Model - Autopilot
LCA Modification: Assisted Lane Biasing and Assisted Lane Change
Combined Controller - 5 DOF
Advanced Topics in Controls for Autonomous Driving- Part 1
Advanced Topics in Controls for Autonomous Driving- Part 2
Industry Project 1 - Applying CV for Autonomous Vehicles using Python
Implementation of an image classification model using MobileNet architecture
2D Object Detection with tensorFlow
Industry Project 2 - Path Planning & Trajectory Optimization Using C++ & ROS
Design Implementation and Comparison of the Graph based Trajectory Planners
Trajectory Planning with Optimization Approach for Autonomous Car in Urban Area
Industry Project 3 - Localization, Mapping and SLAM using Python
Implement an MCL algorithm to simulate the localization of a robot
Implement an occupancy grid map algorithm using Turtlebot
Industry Project 4 - Autonomous Vehicle Controls using MATLAB and Simulink
Design and Develop Adaptive Cruise Control Model in Simulink
Develop an Integrated Automated Driving Model
Post Graduate Program in Autonomous Vehicles at Skill Lync Entry Requirements
- Education: B.Tech/B.E. from any recognized university.
- Proficiency in English (spoken & written) is mandatory.
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Post Graduate Program in Autonomous Vehicles