Electrical · Course

Model Predictive Controls for Autonomous Driving

A 3 month course on model predictive controls for autonomous driving.

Duration
12 weeks
Content
20 hours
Projects
2
Mode
Online
Overview

What you'll
be able to do.

  • The students will have a thorough knowledge of Model Predictive Controls for Autonomous Driving Vehicles and ADAS systems.
  • They can specialize in the domain and gain in-depth knowledge of it.
  • The students are exposed to the modern trends & the standard practices being followed in the industry right now.
  • After completing this course, the students will gain a better understanding of the concepts of Linear Algebra, Controls, Optimization using MPC for diverse problems in ADAS/AD motion planning.
  • Also, the course delves deep into the concepts of "Linearization of nonlinear motion models", "How to use an Optimization Solver" and "Step-by-step approach in building an MPC".
Tools you'll use
CVXOPTPythonCARLA
Curriculum

12 modules,
built with industry.

  1. 01Model Predictive Controls for Autonomous Driving12 modules
    1. Introduction to Linear Algebra and Controls W01
      • Linear Algebra Refresher
      • Basics of Controls
    2. Motion Models for Autonomous DrivingW02
      • Motion Models
      • Lateral and Longitudinal Dynamics
    3. Getting a Hold on LQR for Goal ReachingW03
      • Linear Quadratic Regulator
      • Problem Formulation
      • Stability and Controllability of LQR
      • Convergence of LQR
      • Using LQR and Motion Model for a goal-reaching problem
    4. Introduction to Convex and Non Convex OptimizationW04
      • Basics of Convex Optimization
      • Introduction to Non-Linear Cost Function
    5. Introduction to MPCW05
      • Introduction to MPC
      • Feedback in Optimal Control
      • The Specialty of MPC’s Model
      • Structure of LQR and What MPC Adds to it?
      • Online Feedback
    6. MPC for Goal Reaching ProblemW06
      • Sequential Quadratic Programming
      • Tutorial on CVX_OPT
      • Coding MPC
      • MPC for Multiple-step Problem
    7. Constrained MPCW07
      • QP to MPC
      • Constraints of MPC
      • Static Obstacle Avoidance - Theory
    8. MPC for Collision AvoidanceW08
      • MPC for a Static Obstacle in Practice
      • Multiple Static Obstacle Condition
      • MPC for Dynamic Obstacles
      • Adding Pedestrians to Constraints
    9. Lateral Conditions of MPCW09
      • Lane Keeping Constraints in MPC
      • Lane Change condition
    10. Uncertainty in MPCW10
      • Adding Uncertainty to a Motion Model
      • Types of MPC and Different Cost Formulations
    11. Setting Up CARLA SimulatorW11
      • Setting up CARLA
      • Using CARLA to run a small client-server controller
      • Testing PID in CARLA
    12. Future Scope of MPCW12
      • Future Scope of MPC
      • MPC with Deep Learning Approach
      • Details on Project Implementation
Industry projects

2 projects
you'll ship.

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.

01

Formulating MPC for Non-Holonomic Motion Model

A project on implementing the Model Predictive Control for Non-Holonomic motion model using Python. The project involves formulating the cost function using the CVXOPT framework.

02

Implementing MPC in CARLA Simulator

Implementing obstacle avoidance, lane change, and lane-keeping checks in the CARLA simulator. The controller script should be implemented in the client-server model, to evaluate th…

Fee

One fee.
Everything included.

Become job ready with our comprehensive industry focused curriculum for freshers & early career professionals

Premium
₹40,000
Inclusive of all charges
  • 1 Year Access
  • Personalized Page
  • Live Individual & Group Sessions
  • Personalized & Hands-On
  • Job-Oriented Industry Relevant Curriculum
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