HIGAET Autonomous Systems Engineering
Master perception, localization, planning, and control stacks while engineering safe autonomous vehicles and drones through HIGAET Practical Training applied simulation projects.
Duration
10 weeks · 6-8 hours/week
Level
Advanced
Delivery
Online
Status
Open for enrollment
Why this technology matters.
Autonomous systems engineering is the stack that lets vehicles and drones perceive, localize, plan, and act on their own, and it matters now because safe autonomy depends on disciplined simulation and testing before anything flies or drives. You will learn each layer — perception, localization, planning, control — as pieces of one safety-minded system.
It is used for simulated vehicles and drones that detect and track objects, localize with sensor fusion and mapping, plan paths through dynamic scenes, and execute steering, braking, and flight commands. It solves structured autonomy and repeatable testing well, but it does not remove edge cases and safety review, simulation does not guarantee real-world performance, and perception does not fix bad maps or failed sensors.
By the end you will be able to build a perception pipeline for detection, tracking, and segmentation, a localization and mapping workflow with sensor fusion, and a planning-plus-control stack for navigation and steering, braking, and flight in dynamic environments.
Why this course exists
Watching a simulated vehicle follow a perfect route hides the real work: noisy perception, localization drift, unpredictable obstacles, and control limits. This course teaches the arc from sensing to perception to localization to planning to control to simulated validation, so you can engineer autonomy that is tested, explainable, and safety-aware.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and data libraries
- Familiarity with sensors and control basics
- Basic linear algebra and statistics
Technologies & tools
Skills you'll gain
A 10 weeks arc, module by module.
- Module 01
Module 01 — Foundations: autonomy levels, architectures, and safety
- Module 02
Module 02 — Core: sensors, calibration, and data synchronization
- Module 03
Module 03 — Core: perception, detection, and tracking algorithms
- Module 04
Module 04 — Engineering: localization, SLAM, and HD maps
- Module 05
Module 05 — Engineering: behavior planning and trajectory generation
- Module 06
Module 06 — Advanced: vehicle and flight control systems
- Module 07
Module 07 — Advanced: simulation, datasets, and edge-case testing
- Module 08
Module 08 — Production: validation, monitoring, and fleet operations
- Module 09
Module 09 — Capstone: engineer and validate an autonomous system in simulation
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build perception pipelines for detection, tracking, and segmentation
- Design localization and mapping workflows with sensor fusion
- Develop path-planning and decision logic for dynamic environments
- Deploy control systems for steering, braking, and flight
- Integrate simulation environments, datasets, and middleware
- Evaluate safety cases, failure modes, and operational boundaries
- Secure autonomy stacks against sensor spoofing and software faults
- Automate scenario testing, regression suites, and performance metrics
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Perception pipeline for detection and tracking
- Project 02
Localization and mapping workflow with sensor fusion
- Project 03
Path-planning logic for dynamic environments
- Capstone
Autonomous vehicle or drone stack with steering and flight control
Speak the language first.
- Perception pipelines
- Vision and sensor stages that detect, track, and segment objects around a vehicle or drone.
- Localization
- Estimating the vehicle's exact position by matching sensor data to a map.
- Mapping
- Building and updating a spatial model of the environment the system navigates.
- Sensor fusion
- Merging camera, lidar, radar, and inertial data into one reliable world model.
- Path planning
- Choosing a safe, efficient route that accounts for obstacles and traffic rules.
- Decision logic
- Rules and policies that select actions such as yielding, overtaking, or hovering in dynamic scenes.
- Steering and braking control
- Low-level controllers that convert planned trajectories into wheel and brake commands.
- Flight control
- Stabilization and guidance loops that keep a drone on its planned path in wind and uncertainty.
- Simulation testing
- Evaluating autonomy stacks in virtual scenarios before running on physical vehicles.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Detections flicker or miss objects in poor light
Audit labeled edge cases, augment training data, and fuse complementary sensors to cover the gap.
Localization drifts in tunnels or featureless areas
Check sensor calibration and map freshness, then blend inertial odometry until features return.
Planner freezes or oscillates in dense traffic
Simplify the scenario, tune prediction horizons and safety margins, then re-test in simulation.
Vehicle overshoots steering or brakes harshly
Retune controller gains on logged trajectories and enforce acceleration and jerk limits.
Simulation passes but physical tests behave differently
Compare sensor noise and timing between sim and hardware, then close the gap with calibrated models.
Before you move on, you should be able to
- Explain how perception, localization, planning, and control form an autonomy stack
- Build perception pipelines for detection, tracking, and segmentation
- Design localization and mapping workflows with sensor fusion
- Build path-planning and decision logic for dynamic environments
- Deploy control systems for steering, braking, and flight
- Evaluate autonomy runs in simulation for safety and edge cases
- Deploy tested stacks from simulation to vehicles and drones with monitoring
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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