Advance your career into the world of AI and Self-Driving Cars
DeepMap is a famous self-driving company in Silicon Valley which provides HD mapping and localization services. They will design certain projects for this course and offer interview opportunities for top students.
Want to be a software engineer working on self-driving cars? This course will provide you with core software development skills coupled with the state of the art in planning and decision making technologies for self-driving cars.
Partnering with automotive companies, we are well positioned to build your confidence and readiness for developing state of the art autonomous navigation systems.
Our professional post-class career services will also get you well-prepared in entering self-driving car industry.
Your instructor is not only your partner-in-code, but a seasoned industry-expert who is as dedicated to your success as you are.
We invest as much in you as you do in us. We put a whole team of support behind you, including: instructors, program manager, career coach, and career services manager.
Even after graduation, we’re still invested in you. Your very own career coach will spend the next six months helping you navigate the job market, apply to positions, and ace your interviews.
LaiOffer's track record proven Software Development traning + RobotWit's pioneering autonomous navigation expertise
Focus on software development fundamentals catered towards the skills and techniques widely used in developing autonomous navigation systems, which makes it essential to the course and your future career in the industry.
Learn the essentials and the key challenges of building a self-driving car, and the cutting-edge solutions, techniques and algorithms that modern automomous navigation systems are leveraging to overcome such challenges.
Learn how to install ROS and perform basic and intermediate tasks such as creating visual markers, manipulating reference frames, and troubleshooting modules.
Key Outcomes: You will be familiar with a popular de facto simulator for planning and decision making for robots and self-driving cars. After all the projects, you will be an expert on using ROS for developing planning and decision making modules for self-driving cars.
Develop key foundational planning algorithms that are based on A* to navigate in a discretized known and unknown maps.
Key Outcomes: You will be proficient with the A* search algorithm and its application in self-driving car applications. You will also more easily and better understand more advanced cutting-edge algorithms, as A* forms the foundations for those planning and decision-making algorithms.
Develop planner to navigate through multi-lane highway with multiple static obstacles; design motion primitives to allow efficient transition between lanes and fast planning times.
Key Outcomes: You will be experienced in enhancing and adapting the A* algorithm, and successfully implementing a planning module that enables a self-driving car to change lanes while taking into account other, currently static, vehicles on the highway.
Improve your previous planner to handle dynamic obstacles with known trajectories; Safely plan trajectories in the presence of faster and slower vehicles.
Key Outcomes: You will have adapted your planner to take into account other vehicles that are now moving at varying speeds on the highway, overtaking slower vehicles and moving away from faster vehicles.
Develop planner to account for uncertainty in the motion of another vehicle; Add actions to disambiguate possible uncertainty; Generate trajectories that proactively seek to mitigate uncertainty.
Key Outcomes: You will have learned how to predict the intentions and motions of other vehicles and take them into account to find safe trajectories for your self-driving car.
Focus on software development fundamentals catered towards the skills and techniques widely used in developing autonomous navigation systems, which makes it essential to the course and your future career in the industry.
Frequency: 1 month, 4 sessions/week, 2-3 hrs/session
C++ Basics
- basic types, references and pointers, user-defined types
- C++ program structure, compilation and namespace
- class and its hierarchies, virtual functions
AWS and GitLab setup and introduction C++ Basics II
- constructor and destructor
- copy and move operations
- template introduction
C++ Memory management
- storage class, resource management, smart pointers
STL containers and iterator
STL algorithms
- Lambda and customizing algorithms
- Callbacks
Graph Search Algorithms I
- Queues, stacks, and heaps
- Breadth first search, Depth first search
Graph Search Algorithms II
- Dijkstra’s, A*, Weighted A*
Graph Search Algorithms III
- Adaptive A* + G-FRA*
- Any-angle Search: Theta*
Probability Review
- Probability review, Bayes theorem
C++ Project Preparation
- C++ Compilation Units, namespaces, I/O
- How to design the program
Project 1 – Implement A* and Adaptive A* on gridworlds
Learn the essentials and the key challenges of building a self-driving car, and the cutting-edge solutions, techniques and algorithms that modern automomous navigation systems are leveraging to overcome such challenges.
Frequency: 3 months, 4 sessions/week, 2-3 hrs/session
Intro and Overview of Autonomous Navigation
Math Review
ROS transforms
Trajectories and Vehicle Models
Environment Models
PID control
Implementation of Controls
Planning
State space representations
Hierarchical planners
Heuristic search based planners
Advanced search based planners
Practical planning considerations
Trajectory planning
Sampling based planning
Application of sampling planners
Planning with Uncertainty
Overview of MDPs
Overview of POMDPs
Reinforcement Learning
Application to autonomous driving
Vision/GPS/IMU models and systems
Overview of LIDAR systems
Kalman Filters
Particle Filters
Mapping
Reviews
Final Exam
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