Autonomous Systems and Self-Driving Technologies Corporate Training
Give your engineering team the hands-on autonomy skills to build self-driving technology.
Build the self-driving technology skills your engineering team needs. Edstellar's Autonomous Systems & Self-Driving Technologies corporate training program helps your engineers master perception, sensor fusion, planning, and control through instructor-led sessions and hands-on labs with real driving data. As the corporate training company enterprises trust for emerging-technology programs, we tailor every workshop to your industry, platforms, and goals.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build autonomy-ready engineering teams
THE EDSTELLAR AUTONOMOUS SYSTEMS CAPABILITY MODEL
Baseline autonomy fluency
Assess how your engineers rate perception, localization, planning, and the autonomy stack.
Set one autonomy standard
One shared language for sensors, fusion, planning, and control across the engineering team.
Practice on real data
Hands-on labs use driving data and simulators to make perception, fusion, and control concrete.
Prove the capability lift
Baseline and capstone simulations measure each engineer's grasp of the autonomy stack.
Scale across programs
One autonomy capability rolls out across teams and product lines as your roadmap grows.
Need more than one program?Get a group quote
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Program Overview: What is Autonomous Systems and Self-Driving Technologies Corporate Training?
Autonomous Systems & Self-Driving Technologies is Edstellar's instructor-led technical program for engineers and technical leaders building self-driving systems. Participants master sensor technologies, perception with detection, segmentation and tracking, plus sensor fusion and localization. The program covers planning, navigation and control, along with safety, validation, and the regulatory pathways to deployment.
Training is hands-on from the first module. Participants work through guided labs on sensor data, perception pipelines, and fusion algorithms, then build planning and control stacks for realistic driving scenarios. Instructors coach the full autonomy pipeline with direct feedback on every exercise. The program closes with an autonomous systems capstone integrating the stack end to end.
Self-driving programs stall the same way: perception that works in the lab, planning nobody validated, safety treated as paperwork at the end. This program teaches full-stack discipline, perception proven on real sensor data, planning tested in scenarios, and safety argued from evidence. Everything is practiced on realistic autonomy problems, so the stack earns its road time.
The capstone integrates the full autonomy stack: perception, fusion, localization, planning, and control brought together on a realistic self-driving problem and validated against safety criteria. Engineers return having built the pipeline end to end, the difference between knowing the modules and shipping the system.
Across seven modules, your engineering team moves from autonomy foundations to a full-stack self-driving capstone. Sessions use real driving data, simulation environments, and scenario-based testing, so every technique transfers directly to your vehicle, robotics, or automation programs.
Delivered as a hybrid instructor-led program, it fits around your calendar, with scheduling set along with you, and content tailored to your sensor stack, platforms, and operational domains.
What's Included in this Workshop for Your Enterprise Teams
- 1
Autonomy stack foundations
SAE levels, ODDs, and the end-to-end self-driving pipeline.
- 2
Perception and sensor labs
Build and tune detection, segmentation, and tracking on driving data.
- 3
Fusion and localization practice
Fuse sensors and keep vehicles localized in simulation.
- 4
Planning and control labs
Plan trajectories and close the control loop in scored scenarios.
- 5
Safety capstone
Assemble the full stack and defend your safety argument.
- Training delivered at any of your offices globally
- 10+ languages
- Schedule around your calendar
- Group size set along with you
- Run as one cohort or split into batches
- Instructor-led (virtual/onsite/hybrid)
- Recurring and annual options
- Trainer sourcing, matching and logistics
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
- Plan from 2000+ Industry-ready Training Programs
- Hands-On Labs on Live Infrastructure, Led by Industry Experts
Autonomous Systems and Self-Driving Technologies Course Topics and Program Outline
Seven modules follow a clear learning path from autonomy foundations to a full-stack capstone. Each session builds on the last, with hands-on labs on driving data and simulators that turn autonomy concepts into working engineering skill.
Built by Edstellar with autonomy and robotics practitioners
Built by practitioners who design and validate autonomous systems in production, shaped around your stack and use cases.
Baseline each participant's autonomy systems readiness before module 01 begins.
Build strong self-driving foundations in instructor-led sessions for your team.
Reinforce skills through guided hands-on labs on perception, sensor fusion, and simulation.
Apply self-driving skills to real-world scenarios and a capstone autonomy stack project.
A short pre-training assessment sets each participant's entry level, so the program starts where your team actually is. The rest is weighted toward hands-on practice: perception pipelines, sensor fusion, and simulation-driven planning, and realistic scenarios drawn from your team's work, not slides.
Delivered as
Entry level set by the assessment. Depth and schedule customized to your team.
Map the SAE autonomy levels and operational design domains for self-driving systems, then position where your team's products and projects sit on the autonomy roadmap.
learna)SAE autonomy levels 0 to 5: what changes at each level
b)Operational design domains (ODDs) and why they define capability
c)Autonomous systems beyond cars: robotics, drones, industrial vehicles
d)Industry landscape: AV stacks, open platforms, and key players
Work through the full self-driving sensor suite and learn how each sensor contributes to perception. Labs cover sensor geometry, calibration routines, and reading real sensor data.
learna)Cameras, LiDAR, radar, ultrasonic, and GNSS: strengths and limits
b)Sensor geometry, mounting, and coverage modeling
c)Intrinsic and extrinsic calibration workflows
d)Interpreting real LiDAR point clouds and radar returns
e)Sensor degradation: weather, occlusion, and failure modes
Build a working perception pipeline in guided labs: run pretrained detectors on driving data, tune detection outputs, and track objects across frames.
practicea)2D and 3D object detection with neural networks
b)Semantic segmentation for drivable-area mapping
c)Multi-object tracking across frames
d)Sensor-to-perception data pipeline design
e)Evaluating perception: mAP, precision, recall on driving data
Fuse camera, LiDAR, and radar streams with filtering techniques and build a localization stack that keeps a vehicle positioned on a map. Labs use simulated and recorded driving data.
practicea)Kalman and extended Kalman filters for state estimation
b)Fusing camera, LiDAR, radar, and GNSS measurements
c)Localization against HD maps and SLAM fundamentals
d)Tracking fused objects through occlusion and clutter
e)Validating fusion output for safety-critical decisions
Take fused perception output and turn it into motion: plan routes and trajectories, then close the control loop. Labs run in a driving simulator with scored maneuvers.
practicea)Global route planning and behavior planning
b)Trajectory generation and collision avoidance
c)Longitudinal and lateral control: PID and MPC basics
d)Simulation-based testing of planning and control
e)Handling edge cases: intersections, merges, pedestrians
Apply safety engineering practice to autonomy: work through hazard analysis, validation strategies, and the regulatory landscape your deployment teams must navigate.
learna)Functional safety for autonomy: ISO 26262 and SOTIF
b)Scenario-based testing and mileage-equivalence strategies
c)Simulation, closed-course, and public-road validation
d)Regulatory frameworks and deployment approval pathways
Integrate the full program in a capstone: configure a complete autonomy stack in simulation, from perception through planning and control, then present your system and safety argument to the cohort.
applya)Assembling the end-to-end autonomy stack
b)Full-system simulation runs and failure injection
c)Building the safety argument for your system
d)Capstone presentation and expert review
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
Your engineering team will leave able to build perception pipelines, fuse sensors, plan motion, and validate autonomous systems. Each outcome is practiced in hands-on labs and shown in the capstone.
Design the self-driving sensor suite
Select and configure cameras, LiDAR, radar, and GNSS for an operational design domain, and keep them calibrated.
Build perception pipelines
Run 2D and 3D detectors, segmentation, and multi-object tracking on driving data and evaluate the results.
Fuse sensors for localization
Apply Kalman filtering to fuse multi-sensor streams and localize a vehicle against maps and SLAM.
Plan and control vehicle motion
Generate safe trajectories and close the control loop with PID and MPC techniques in simulation.
Test autonomy in simulation
Run scenario-based tests, inject failures, and score planning and control decisions before road testing.
Argue safety for autonomy
Apply ISO 26262 and SOTIF thinking to hazard analysis, validation, and deployment approvals.
Integrate the full autonomy stack
Connect perception, fusion, planning, and control into a working system and present it with a safety case.
Teams that Benefit from this Autonomous Systems and Self-Driving Technologies Group Training
This program is built for engineering teams building autonomy, from perception and controls engineers to systems and validation teams. Solid software or systems engineering experience is expected; content adapts to each cohort, so every engineer practices the autonomy skills their role actually owns.
Own the eyes of the autonomy stack.
Perception engineers master sensor pipelines, detectors, segmentation, and tracking, so perception output stays reliable in real driving conditions.
Workflow friction
- Detectors fail in edge-case conditions
- Sensor changes break perception pipelines
- Hard to evaluate perception quality in deployment
How the program helps
- Build and tune perception on real driving data
- Keep pipelines robust across sensor setups
- Measure perception with the right metrics
Practice use cases
- Perception pipelines
- Detector tuning
- Tracking evaluation
Turn perception into safe motion.
Planning and controls engineers master trajectory generation, vehicle control, and system integration, so perception output becomes safe, smooth motion.
Workflow friction
- Planning breaks in complex scenarios
- Control tuning is slow and risky on hardware
- System integration exposes late failures
How the program helps
- Test planning and control in simulation first
- Tune control loops with scored feedback
- Integrate the full stack with confidence
Practice use cases
- Motion planning
- Control tuning
- Full-stack integration
Ship autonomy you can defend.
Safety and validation teams master scenario-based testing, functional safety, and regulatory pathways, so autonomy programs reach deployment with evidence.
Workflow friction
- Validation coverage feels incomplete
- Safety arguments lack structure
- Regulatory pathways are unclear
How the program helps
- Build scenario-based validation plans
- Structure hazard analysis and safety cases
- Navigate deployment approvals
Practice use cases
- Scenario testing
- Safety cases
- Deployment readiness
Each cohort works through role-relevant hands-on labs, then leaves with working autonomy skills for day-to-day engineering.
Prerequisites: This program expects solid software or systems engineering experience, including comfort with Python and basic linear algebra. Prior robotics, computer vision, or automotive experience helps but is not required. Content adapts to each cohort's platforms and experience, so teams from adjacent domains ramp up together.
Software Engineer to Full-Stack Autonomy Engineer
The program takes engineers from autonomy basics to full-stack delivery: foundation concepts, intermediate perception and fusion labs, then advanced planning, control, validation, and a capstone integration.
Engineers grow from autonomy foundations to full-stack integration as the modules progress.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Virtual, Onsite and Hybrid
Every mode is shaped around your platforms and sensor stack before delivery. The curriculum, hands-on labs, and capstone stay the same in each format, so what your engineers can do after training never depends on how you run it.
Hybrid Training
Live instruction across office and remote sessions, with hands-on labs on driving data and driving simulators coached throughout. Your engineers build perception pipelines, tune fusion, and run a full-stack capstone with instructor guidance. Best for multi-site teams.
- Live instruction across office and remote sessions
- Hands-on labs with driving data and simulators
- Guided capstone with expert review
- Best for engineering teams across sites and time zones
Onsite Training
Your instructor delivers at your facility, with the lab environment set up for your engineering team. Participants work through perception, fusion, and control labs with direct coaching. Best for co-located teams.
- Instructor delivers at your facility
- Lab environment set up for your team and platforms
- Direct coaching during hands-on labs
- Best for co-located teams wanting maximum interaction
Live Virtual Training
Live virtual sessions on Zoom or Microsoft Teams, with remote access to the lab and simulation environment and screen-shared guidance through every task. Your engineers collaborate on autonomy labs in real time. Best for distributed teams.
- Live sessions on Zoom or Microsoft Teams
- Remote access to the lab and simulation environment
- Screen-shared guidance through every task
- Best for distributed engineering teams
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Frequently Asked Questions
Share your team size, preferred dates, and delivery mode in the lead form. We reply with a tailored proposal covering scope, schedule, and pricing for your group.
Both. Choose live virtual sessions on Zoom or Teams, onsite delivery at your office, or a hybrid mix across sites and time zones.
The standard program runs 19.5 hours of instructor-led sessions with hands-on labs, typically over three days. We can reshape the schedule around your calendar.
We recommend 12 to 20 engineers per cohort for strong lab coaching. Larger engineering teams can run as parallel batches.
Yes. We shape sensor stacks, driving data, and the capstone around your platforms, vehicles, and operational domains before delivery.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
This is the entry point for teams working on self-driving technology. You first learn how autonomous systems work end to end, then study LiDAR sensing, combine sensors through fusion algorithms, and finally validate ADAS features. Each step narrows from the whole system to the evidence that it is safe.
Why Choose Edstellar as Your Autonomous Systems and Self-Driving Technologies Enterprise Training Provider
If your engineering team needs to build self-driving capability, start here. Tell us about your platforms, timelines, and preferred delivery mode, and we will shape a program around them.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Content shaped around your perception and planning use cases, so every session maps to your real autonomy needs.
Proven Methodology
5+ years delivering corporate training to perception and systems engineers across industries and regions.
Practice-Based Learning
5 hands-on labs across 7 modules. Your team builds perception and sensor-fusion prototypes with instructor guidance.
Expert Trainers
5,000+ certified trainers matched to your autonomy stack, with a full trainer profile shared before your program is booked.
Global Scale
One autonomous-systems program across your offices worldwide, in hybrid, onsite, or live virtual formats.
Measurable Outcomes
Baseline-to-capstone measurement and Skills Matrix reporting keep your autonomy capability lift visible.
Integrate Self-Driving Technologies Through Sensor-to-Control Simulation-Based Engineering Practice
Our self-driving technologies training follows information from vehicle sensors to motion decisions. Participants explore perception, fusion, localization, and planning before integrating an autonomy stack in simulation. Technical reviews connect observed behavior with the operational design domain and safety assumptions, keeping the distinction between a working simulation and deployment readiness explicit.
- Step 01
Consult
We assess your software and systems background alongside autonomy goals, identifying development needs in sensor data, perception, localization, planning, control, and validation.
- Step 02
Plan
We establish autonomy levels and operating domains before sensor calibration, perception pipelines, fusion and localization, then planning, control, and the integrated simulation capstone.
- Step 03
Align
We align the technical depth with participants’ Python and linear-algebra experience, confirming recorded driving data and simulator access for the practical sequence.
- Step 04
Customize
We adapt sensor and maneuver scenarios to representative operational domains, selecting conditions that make perception errors, localization uncertainty, and planning trade-offs visible.
- Step 05
Execute
Participants run detectors, track objects, fuse sensor streams, and evaluate simulated maneuvers with feedback on subsystem interactions and the evidence behind safety claims.
- Step 06
Evaluate
We review the simulated stack for connected subsystem behavior, measured maneuver performance, documented operating assumptions, and a safety argument that acknowledges validation limits.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
Autonomous Systems & Self-Driving Technologies Training Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request Autonomous Systems and Self-Driving Technologies Training for Your Team
Share your team size, autonomy goals, preferred delivery mode, and timelines. We will respond with a tailored training plan covering scope, schedule, and pricing, and confirm trainer availability.