Become on-the-job ready with the basics of perception, localization, control, and technology integration
Control your future in the autonomous mobility megatrend. Stand out in the market by describing how you integrated sensor technologies and programmed code to run an autonomous electric vehicle. Be ready to perform on day 1 of your new career.
There is no denying the autonomous revolution is rapidly accelerating. Original equipment manufacturers (OEMs), Tier 1 and Tier 2 suppliers are increasing their rates of production on autonomous fleets, while the largest constraint is the complexity to integrate these technologies in a safe and trustworthy manner. The transportation industry is in desperate need of engineers with the knowledge to safely integrate autonomous subsystems into a vehicle and the practical hands-on experience of overcoming these technological complexities.
Meet Tigress. Tigress is a fully-autonomous electric vehicle provided by our industry partner PerceptIn. Tigress comes complete with a fully-integrated sensor package that enables fully-autonomous operation. Tigress combines patented vision-based sensor fusion with a patented modular computing system to provide a safe and reliable method of transport in low-speed environments. It uses visual intelligence technology, including a global navigation satellite system (GNSS) module, sonar, and radar.
During the Intro to Autonomous Mobility course, students will implement sonar, radar, LiDAR, and sensor technologies directly on Tigress in order to demonstrate a fully-autonomous vehicle by the end of the 9 weeks.
Learn about Python, CAN Communications, and Robot Operating Systems (ROS) and how it provides a flexible and unified software environment. Work with sensors like sonar, LiDAR, radar, and computer vision and learn how to program devices and manage signal processing. Finally, learn about the industry leading simulation software CARSIM, by Mechanical Simulation Corporation, which will be used throughout the course.
Learn how machine learning and its various concepts are being used in autonomous vehicles, focusing on the deep learning aspect and how machine learning goes together with perception. Learn to use the power of machine learning techniques where OpenCV fails to work. Gain experience with object recognition in real time scenarios. Discuss the challenges faced for deep learning and how to solve them as a team. Train your own machine learning model, integrate the model with ROS and a camera, then pair with all knowledge from previous modules to develop algorithms to control the autonomous vehicle.
Coordinate systems, map making, and necessary formulas. Take a deep dive in the GNSS System and why it is a key enabler of autonomous mobility. In this module, analyze accuracy performance between standard and RTK enhanced GPS data, understand how to connect to a live GPS receiver stream and how to analyze this data, and collect GPS data from a full-size autonomous electric vehicle as it maneuvers a course. Also, record data from a second receiver and determine how it calculates orientation.
Learn about Drive by Wire (DBW) and how it enables autonomous vehicles. Become familiar with throttle, brake, and steering actuators which are essential to autonomous vehicle operation. Then work with actual DBW components on benches, learning about how the steering and braking mechanisms and actuators work. Implement a simple steering angle controller (PID) to position the wheels, gathering data to manage both the position of the steering angle and velocity. Install your steering controller algorithm onto an autonomous electric vehicle allowing it to either directly control the steering or run in “safe parallel” where it computes steering actuator commands compared to the vehicle’s commands.
In this module, bring it all together by creating models for sensor fusion, path planning, and vehicle control using MATLAB Simulink and CARSIM. Dive into Kalman Filtering and then develop and demonstrate building an IMU GPS filter structure and assess performance of the filter, using MATLAB control system and sensor fusion toolboxes. Further, learn discreet path planning and prediction concepts, followed by trajectory generation, then ultimately generate a unique path plan. Learn about closed-loop feedback controls, understanding the Model Predictive Control formulation, and then finally gain the experience and knowledge of the integration and tuning of advanced controls in the simulation environment, CARSIM.
Apply all your learning in the previous 8 weeks as part of a project team by tackling a tough challenge on one of our autonomous electric vehicles. Perhaps you will integrate a new sensor, such as LiDAR, onto the vehicle and must create a path plan to enable autonomous functioning, or perhaps your task will be to improve the machine learning functionality of the vehicle’s controller. Whatever the challenge, your learning will culminate with an experience that cannot be found in any other training program, one which can be used to communicate
a grasp of autonomous mobility technologies in front of hiring managers looking for someone with your newly acquired skills. An IEEE certificate of completion, with continuing education units, will be awarded upon successful completion
of the course.
We Offer This Course in Both a Full-Time Model and a Nights & Weekends Model.
The full-time program is designed for anyone who is currently job searching, between jobs, or who has just finished a university degree. This program mimics a full-time, 9-5 work environment for participants to collaborate in teams to find effective solutions.
Students will perform Modules 1 - 9 over a full-time Monday - Friday schedule.
The nights & weekends program is designed for engineers who are looking to develop their professional skills after hours; whether it’s after your 9-5 job, university studies, or busy schedule, this program will allow you to train in a new skill set while maintaining current responsibilities.
Students will perform Modules 1 - 8 over a 9-week period of Wednesday evenings and Saturday/Sunday sessions.
At LHPU we understand that higher education costs can be overwhelming. That is why we offer two flexible payment plans with features that ease the burden.
Pay 100% of the tuition upfront, before the start of the class, and receive a substantial discount off the list price.
This plan allows you to make a small deposit to reserve your spot in one of our trainings, then pay a low $25 monthly payment upon completion of training. Once you secure qualifying employment, you’ll make larger, yet still manageable, monthly, interest-free payments until your entire tuition is paid in full. All payments made go toward your balance. It's that easy!
LHPU Training Solutions is honored to have our training curriculums approved through the Institute of Electrical and Electronics Engineers (IEEE), "the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity." Upon successful completion of an LHPU course, participants will receive an IEEE Certificate of Completion with CEUs and PDH's awarded.
In addition to being immersed in the perfect learning environment, you will also be surrounded by mentors to help you master the soft skills needed to excel as an automotive engineer. LHPU is about more than just learning new skills as an engineer, it’s about taking your career to the next level.
Interested in learning more? Hear straight from a recent alumna on how the Intro to Autonomous Mobility course propelled her career!