Self-Driving Cars Specialization

Launch Your Career in Self-Driving Cars. Be at the forefront of the autonomous driving industry.

Instructors: Steven Waslander +1 more

What you'll learn

  •   Understand the detailed architecture and components of a self-driving car software stack
  •   Implement methods for static and dynamic object detection, localization and mapping, behaviour and maneuver planning, and vehicle control
  •   Use realistic vehicle physics, complete sensor suite: camera, LIDAR, GPS/INS, wheel odometry, depth map, semantic segmentation, object bounding boxes
  •   Demonstrate skills in CARLA and build programs with Python

    • Skills you'll gain

  •   Automation
  •   Predictive Modeling
  •   Global Positioning Systems
  •   Artificial Neural Networks
  •   Spatial Data Analysis
  •   Hardware Architecture
  •   Deep Learning
  •   Computer Graphics
  •   Scenario Testing
  •   Software Architecture
  •   Machine Learning Methods
  •   Embedded Software

    • Specialization - 4 course series

    You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry.

    This course will introduce you to the main planning tasks in autonomous driving, including mission planning, behavior planning and local planning. By the end of this course, you will be able to find the shortest path over a graph or road network using Dijkstra's and the A* algorithm, use finite state machines to select safe behaviors to execute, and design optimal, smooth paths and velocity profiles to navigate safely around obstacles while obeying traffic laws. You'll also build occupancy grid maps of static elements in the environment and learn how to use them for efficient collision checking. This course will give you the ability to construct a full self-driving planning solution, to take you from home to work while behaving like a typical driving and keeping the vehicle safe at all times. For the final project in this course, you will implement a hierarchical motion planner to navigate through a sequence of scenarios in the CARLA simulator, including avoiding a vehicle parked in your lane, following a lead vehicle and safely navigating an intersection. You'll face real-world randomness and need to work to ensure your solution is robust to changes in the environment. This is an intermediate course, intended for learners with some background in robotics, and it builds on the models and controllers devised in Course 1 of this specialization. To succeed in this course, you should have programming experience in Python 3.0, and familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses) and calculus (ordinary differential equations, integration).

    State Estimation and Localization for Self-Driving Cars

    Visual Perception for Self-Driving Cars

    Motion Planning for Self-Driving Cars

    ©2025  ementorhub.com. All rights reserved