Robotics & Autonomous Systems · Practical career preparation

Autonomous Driving & Self-Driving Systems Engineer

Perception, Planning, Control & Scenario Validation

Build a driving function—and test the situations that challenge it. Connect perception, localization, planning and control in a bounded autonomous-driving simulation. Define where the function is intended to operate, measure its behavior and investigate difficult scenarios with reproducible evidence.

12 core modules Diagnostic-based preparation Three guided projects One independently assessed capstone
RoboEdify track record
10,000+
alumni transformed
1,000+
hiring partners
4.8/5
average class rating
87%
placed in 6 months
10+
years of training
Where our AI alumni work
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer

What does this engineering pathway involve?

An autonomous-driving engineer develops or validates software that supports vehicle perception, decision-making and motion. Different roles own different parts of the stack. This course builds integration and scenario-testing competence for a defined simulated driving function, rather than promising complete self-driving capability.

Learning approach

Learn from behavior you can inspect and failures you can reproduce.

Each practical task includes an explicit contract, a working baseline and a failure to investigate. You use logs, plots or recordings to explain the result, then test your correction. The final assessment includes an unfamiliar debugging task so that a rehearsed demonstration is not the only evidence of competence.

Learning format

Learn through classes, labs and individual feedback.

The program combines live mentor-led classes, guided labs, project work and support sessions. An advisor can explain the current on-campus, online or working-professional format and the course-specific lab arrangements before enrollment.

Simulation, replay and physical demonstrations are identified separately. The page does not imply that a simulated result has already been reproduced on hardware.

On-campusLive onlineWorking-professional format
Who should join

Match the starting point to your existing skills.

Graduates should demonstrate robotics-software, mathematics and perception/control readiness before the advanced core. Working automotive, embedded and robotics engineers use the diagnostic to identify gaps. A foundation bridge does not replace prior engineering competence across every stack layer.

Graduates and working engineers follow the same practical exit requirements. Previously demonstrated foundations can be recognized; broader gaps receive a separate learning plan before the core.

Learning outcomes

Build skills you can demonstrate.

Define a bounded driving taskState operating assumptions, limits and measurable acceptance criteria.
Evaluate sensor and perception outputsInspect calibration, timestamps and error cases.
Analyze localization and trackingUse consistent coordinates and uncertainty-aware inputs.
Integrate planning and controlConnect behavior choices to feasible trajectories and execution.
Build scenario-based testsMeasure collisions, route outcomes, tracking and interventions.
Explain safety evidence and limitsDistinguish a teaching assessment from road approval or standards certification.
Preparation areas

What you practise before the core.

C++/Python and LinuxWrite tested code, reproduce environments and inspect runtime logs.
Robotics integrationUnderstand ROS 2, frames, timestamps, transforms and sensor interfaces.
Estimation, control and mathematicsProbability, coordinate geometry, kinematics and feedback foundations.
Perception and data readinessEvaluate model outputs, understand dataset splits and inspect camera/LiDAR data.

Graduates and working engineers follow the same practical exit requirements. Previously demonstrated foundations can be recognized; broader gaps receive a separate learning plan before the core.

Course curriculum

Twelve modules, from foundations to an independent engineering assessment.

Each module includes a practical task, a failure scenario, deliverables and an assessed outcome.

01

Autonomous-driving architecture and ODD

Engineering core
+
Topics
•Stack boundaries
•ADAS versus autonomous functions
•Operational design domain
•Assumptions
•Interfaces
•Scenario requirements
Hands-on lab
Define a low-speed simulated driving function and its permitted road, actor and environmental conditions.
Failure scenario you solve
A system is evaluated outside its stated conditions without recording the difference.
You build
ODD definition and acceptance matrix.
Assessed outcome: Explain which conditions are covered and which require a different evaluation.
02

Vehicle models and coordinate systems

Engineering core
+
Topics
•Bicycle-model concepts
•Steering and acceleration limits
•Frames
•Ego state
•Timestamp alignment
•Actuator interfaces
Hands-on lab
Compare predicted and simulated motion for a bounded steering/velocity input.
Failure scenario you solve
A steering-unit or frame error produces an apparently unstable controller.
You build
Vehicle-model notebook and interface tests.
Assessed outcome: Identify a unit/convention mismatch before tuning the controller.
03

Sensors, calibration and data pipelines

Engineering core
+
Topics
•Camera/LiDAR/IMU/GNSS roles
•Intrinsic/extrinsic calibration
•Synchronization
•Recorded data
•Provenance and held-out splits
Hands-on lab
Inspect a supplied multi-sensor recording and introduce a calibration or timestamp error.
Failure scenario you solve
A correctly detected object is projected into the wrong lane because sensor frames are misaligned.
You build
Dataset manifest, calibration checks and replay procedure.
Assessed outcome: Separate sensing/alignment defects from model errors.
04

Perception and detection evaluation

Engineering core
+
Topics
•Detection/segmentation concepts
•Confidence
•Class metrics
•False negatives
•Occlusion
•Distribution change
•Latency
Hands-on lab
Evaluate a supplied perception baseline on held-out cases and inspect class-specific failures.
Failure scenario you solve
A high aggregate score hides missed vulnerable road users in a difficult subset.
You build
Perception evaluation, error taxonomy and latency report.
Assessed outcome: Report the relevant subset failures rather than only an average metric.
05

Localization, maps and state estimation

Engineering core
+
Topics
•Map frames
•GNSS/IMU/odometry fusion concepts
•Map matching
•Uncertainty
•Pose jumps
•Stale estimates
Hands-on lab
Compare a localization baseline over nominal and degraded recorded/simulated segments.
Failure scenario you solve
A pose jump places the ego vehicle on an incorrect route segment.
You build
Localization traces and quality-gating tests.
Assessed outcome: Explain how invalid localization should affect downstream behavior.
06

Actor tracking and prediction

Engineering core
+
Topics
•Association
•Motion models
•Track lifecycle
•Uncertainty
•Prediction horizons
•Multimodal behavior awareness
Hands-on lab
Track scripted actors and compare a simple prediction baseline under turns and occlusion.
Failure scenario you solve
A disappeared actor is retained as current, or a constant-velocity forecast fails during a turn.
You build
Tracking/prediction metrics and failure cases.
Assessed outcome: Distinguish observation error from a prediction-model limitation.
07

Behavior and route planning

Engineering core
+
Topics
•Route representation
•Lane following
•Stop/yield behavior
•Traffic signals
•Preconditions
•Fallback states
•Rule-based baseline
Hands-on lab
Implement or configure a bounded behavior sequence for route following and stopping in supplied scenarios.
Failure scenario you solve
The planner proceeds because a traffic-signal state is stale or missing.
You build
Behavior contract, state traces and rule tests.
Assessed outcome: Connect each action to current evidence and a defined operating assumption.
08

Trajectory planning and vehicle control

Engineering core
+
Topics
•Path versus trajectory
•Collision checks
•Speed profiles
•Vehicle limits
•Tracking controllers
•Comfort metrics
•Feasibility
Hands-on lab
Compare two bounded tracking/planning settings on the same route and disturbance cases.
Failure scenario you solve
A collision-free geometric path requires steering or acceleration the vehicle cannot deliver.
You build
Trajectory/control configuration and matched tracking report.
Assessed outcome: Explain feasibility and tracking failures without changing the test conditions.
09

Autoware integration and simulation

Engineering core
+
Topics
•Pinned package/container set
•Launch/configuration
•Documented planning simulation
•Vehicle/map models
•Replay
•Component contracts
Hands-on lab
Run and extend a qualified Autoware planning-simulation baseline; trace information across selected stack boundaries.
Failure scenario you solve
A version or message mismatch prevents components from exchanging valid state.
You build
Integration manifest, configuration and baseline scenario logs.
Assessed outcome: Reproduce the chosen stack without assuming arbitrary simulator bridges are compatible.
10

Scenario testing and safety literacy

Engineering core
+
Topics
•Scenario taxonomy
•Nominal/edge/fault cases
•ODD slices
•Fallback
•Hazard reasoning
•ISO 26262/SOTIF scope awareness
•Evidence limits
Hands-on lab
Create a scenario suite with scripted actors, stale input and a changed environment, then write a bounded assurance argument.
Failure scenario you solve
A demo completes one route but has no evidence for the failure conditions named in its requirements.
You build
Scenario catalogue, results and safety-literacy report.
Assessed outcome: Distinguish evidence of tested behavior from certification or proof of public-road safety.
11

Independent driving-function capstone

Capstone
+
Topics
•Frozen low-speed function
•Qualified simulator
•Held-out scenarios
•Behavior/planning/control integration
•Regression and limitations
Hands-on lab
Deliver a simulated lane/route-following function with scripted obstacle and stop scenarios, plus documented fallback behavior.
Failure scenario you solve
A seemingly improved planner reduces average travel time while increasing failures in one scenario class.
You build
Stack configuration/code, scenario runner, metrics and demonstration.
Assessed outcome: Report all required trials and explain regressions under matched conditions.
12

Technical interviews and portfolio defense

Career preparation
+
Topics
•C++/Python reasoning
•Stack diagnosis
•Scenario interpretation
•Controls/perception boundaries
•Individual contribution
Hands-on lab
Diagnose an unfamiliar sensor, state or planning failure and defend the capstone scope.
Failure scenario you solve
An applicant cannot explain whether an incident began in perception, localization or control.
You build
Technical case study and individual assessment.
Assessed outcome: Trace the failure to evidence and propose a testable correction.

Scope note: Depth is one bounded simulated driving function and its validation. Full Level 4/5 autonomy, automotive production deployment, ISO certification, unrestricted road testing and complete ownership of every perception/planning subsystem are not promised.

Tools and methods

Use a focused, compatible engineering stack.

ROS 2, C++ and PythonDevelop integration and analysis components.
Autoware documented simulationRun the selected bounded driving stack.
Recorded camera/LiDAR dataEvaluate sensing and perception independently.
CARLA — bounded optional exercisesStudy sensor/scenario behavior separately unless integration is qualified.
Scenario runners and metricsCompare matched trials and retain failure evidence.
Git and reproducible environmentsTrack versions, configurations and evaluation assets.

The core is simulation and recorded-data based; it does not require a road vehicle. One Autoware-compatible environment is qualified for the capstone. Separate CARLA exercises do not imply a validated end-to-end Autoware/CARLA pairing. Compute access and supported versions will be specified before enrollment.

Projects and portfolio

Three guided projects and an independent capstone.

The guided projects develop across the curriculum and feed the final assessment.

Guided project 1

Sensor and localization audit

Inspect a recorded/simulated multi-sensor set and diagnose frame, timestamp and pose-quality errors.

Portfolio evidence
Dataset manifestCalibration testsLocalization report
Guided project 2

Behavior and trajectory experiment

Implement a bounded stop/route behavior and compare feasible tracking under matched conditions.

Portfolio evidence
Behavior contractTrajectoriesTracking metrics
Guided project 3

Scenario-validation harness

Automate nominal and fault cases on a qualified driving-simulation baseline.

Portfolio evidence
Scenario catalogueRunnerFailure reports
Capstone

Bounded simulated driving function with scenario validation

Integrate a low-speed route-following function with scripted obstacle and stop cases in a qualified simulation. Define its operational design domain, use held-out scenarios and report completion, collisions, interventions and fallback behavior.

What you submit
  • •A frozen scope and acceptance checklist.
  • •Stack configuration, perception/localization analysis and scenario-validation evidence.
  • •Source/configuration files and a readable reproduction guide.
  • •A failure report showing cause, correction and recheck.
  • •A demonstration with complete scenario/trial results and limitations.
  • •An individual walkthrough and live debugging assessment.
What the assessor checks: The project meets the agreed task contract, reports every required test and preserves the distinction between simulation and hardware evidence. You explain failures and your own contribution. Unsupported capability claims, omitted failed trials and an edited demonstration alone do not satisfy the assessment.
Assessment and completion

Demonstrate what you can do.

RoboEdify · Certificate of Completion
Autonomous Driving & Self-Driving Systems Engineer
Presented to
Learner name
Awarded for completing the practical assessments and independently defending the course capstone within its documented scope.
Manikanta Kona
Founder · RoboEdify
ROBO
EDIFY
CERT
30%
Module labs
20%
Practical checkpoints
35%
Capstone
15%
Individual debugging and defense
The proposed completion standard is 70% overall, at least 60% separately in the capstone and individual defense, and completion of all mandatory practical requirements. Feedback identifies the work needed for remediation and reassessment.
This is a proposed RoboEdify course credential. It is not a regulatory operating permission, professional licence or external standards certification.
Career preparation

Prepare for relevant engineering work with evidence you can explain.

Autonomous-Driving Software TraineeBuild bounded integration and debugging experience.
ADAS / Scenario-Validation TraineeDevelop repeatable scenario and failure-analysis skills.
Junior Perception / Planning Integration EngineerWork with defined interfaces and evaluation evidence, subject to prerequisites.

Career preparation includes a reviewed repository, a technical project summary, evidence-based resume statements and individual interview practice. Course completion does not establish senior-role eligibility; employers set their own experience and qualification requirements.

Career support

Build your portfolio. Prepare your profile. Practise your interviews.

Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. The shared hiring-partner network includes Infosys, TCS, Deloitte, Accenture, Cognizant, NTT Data and Capgemini.
01 / PORTFOLIO

Evidence from your own work.

For this course, your portfolio centres on stack configuration, perception/localization analysis and scenario-validation evidence you complete.

02 / PROFILE

Profile and resume preparation.

A reviewed technical project summary, a readable repository and resume statements grounded in your contribution.

03 / INTERVIEWS

Interview practice and introductions.

Technical interview practice, with role-fit introductions where available.

RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.

Institute leadership

Meet the team behind RoboEdify.

MK
Manikanta Kona
Founder, RoboEdify · Enterprise AI Architect
Enterprise AI · Agentic Systems · LLM Platforms · Robotics & Edge AI
15 yrs
ENTERPRISE AI
2,400+
LEARNERS
4.9 /5
RATING

Manikanta brings 15 years of enterprise platform architecture experience from AT&T, Salesforce, Cox Communications and Broadcom. His background includes enterprise platform and AI rollouts for Fortune-500 banks, telcos and insurers, and production agentic-AI deployments for governed case handling.

Education: M.S. in Engineering, Purdue University.

RK
Ravi Krishna
Chief Technologist, RoboEdify · Implementation & Delivery Lead
Enterprise automation · Deployment · Evaluation evidence · Delivery governance
10 yrs
IMPLEMENTATION & DELIVERY
1,800+
LEARNERS
4.8 /5
RATING

Ravi leads RoboEdify's implementation and delivery practice. His background spans enterprise automation programs, deployment, evaluation evidence and delivery governance.

Industry voices from RoboEdify’s AI programs

What employers say about RoboEdify’s AI and robotics graduates.

The following testimonials retain their original program context. They describe AI, robotics and enterprise-program experience, rather than outcomes from this course.

Microsoft logo

RoboEdify grads ramp 40% faster on applied AI projects than typical hires. Best AI engineering pipeline in India.

Aakash Mehta

Aakash Mehta, Partner Programme Lead, Microsoft

Deloitte logo

We've onboarded 80+ RoboEdify alumni in 18 months. Lowest ramp time we've seen for ML plus AI agent practices.

Anita Sharma

Anita Sharma, Senior Manager, Deloitte

Mphasis logo

The programme is comprehensive — predictive ML, LLM systems, plus agentic and robotics work. Grads come pre-trained for enterprise.

Rahul Bhatt

Rahul Bhatt, Solutions Lead, Mphasis

TCS logo

Their agent + robotics track produces engineers who ship production-grade perception and control code on day one. Genuinely rare.

Deepak Pillai

Deepak Pillai, Senior Architect, TCS

Accenture logo

What sets RoboEdify apart is the simulation-to-hardware layer baked into the AI track. Our clients ask for exactly this profile.

Suresh Menon

Suresh Menon, Practice Lead, Accenture

Infosys logo

Their fundamentals prep is rigorous, and the capstone with a real deployed system and safety case is what closes interviews for us.

Vikram Iyer

Vikram Iyer, Director, Infosys

Wipro logo

RoboEdify's AI grads get models into production twice as fast in the first 90 days. Our internal metrics back this up clearly.

Lakshmi Nair

Lakshmi Nair, VP Engineering, Wipro

Cognizant logo

Best AI + robotics pipeline we've sourced from in India. Their projects are production work, not toy code.

Karthik Subramanian

Karthik Subramanian, Engineering Director, Cognizant

Capgemini logo

Strong ML and edge-deployment foundation. Their grads need almost zero ramp time on enterprise engagements with us.

Arun Joshi

Arun Joshi, Practice Director, Capgemini

IBM logo

We've placed 40+ RoboEdify alumni across our AI and automation teams. Strong fundamentals, sharp on the agent stack.

Sanjay Verma

Sanjay Verma, Talent Director, IBM

LTIMindtree logo

ITOM + Predictive Intelligence is exactly the talent gap we've been struggling to close. RoboEdify is filling it for us reliably.

Anjali Desai

Anjali Desai, Practice Head, LTIMindtree

Tech Mahindra logo

Their AI track delivers engineers who navigate data, models and integrations on customer engagements unsupervised.

Ramesh Iyer

Ramesh Iyer, Senior Manager, Tech Mahindra

Cyient logo

Hired 25+ RoboEdify graduates for our AI practice. Strong coding, strong ML depth, sharp on the agent layer.

Geetha Pillai

Geetha Pillai, Talent Acquisition Lead, Cyient

Microsoft logo

RoboEdify grads who blend robotics with Azure OpenAI land production-ready on day one. Rare combination, well-trained.

Priya Reddy

Priya Reddy, Talent Lead, Microsoft

AI alumni across RoboEdify

Meet alumni featured in our AI programs.

SB
Spandana Bala
ML Engineer
Hyderabad · India
Now at · Infosys
NV
Naveen Vedala
AI Agent Engineer
Hyderabad · India
Now at · TCS
TA
Tejashwini Addla
Simulation Engineer
Hyderabad · India
Now at · Deloitte
TD
Tharunesh Dillikar
Robotics Software Engineer
Seattle · United States
Now at · Accenture
MM
Mujahed Mohammed
Edge AI Engineer
Hyderabad · India
Now at · Accenture
BK
Bhargav Kumar Murala
Physical AI Engineer
Hyderabad · India
Now at · Capgemini
SL
Sai Manasa Leburi
Autonomous Systems Engineer
New York · United States
Now at · NTT Data
RD
Rahul Dhamma
Robot Learning Engineer
Hyderabad · India
Now at · Cognizant
Our locations

Come chat with us—on campus or online.

Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave · Hyderabad, Telangana
Call
+91 8142998866
US desk
+1 256 388 7766
Opening hours
Mon–Sun · 7 AM–9 PM
Online
Global
Live online classes and mentorship, with working-professional learning options. This course uses the simulation and lab arrangements described in its tools section.
Format
Live online + mentorship
Options
Working-professional
Learning support

Support when you need to catch up.

Freeze your seat for up to 90 days and rejoin the next class at no extra fee. TAs run catch-up sessions every Saturday, and recordings of every live session are available for the lifetime of your account.

FAQs

Questions about prerequisites, tools and completion.

Is this a complete self-driving car program?+
It develops a bounded simulated driving function and scenario-testing competence. Full vehicle autonomy and production deployment require much broader engineering and validation.
Do I need a vehicle?+
No. The core uses simulation and recorded data; no public-road driving exercise is required.
Will I use CARLA and Autoware together?+
Autoware documented simulation is the proposed integration baseline. CARLA can support separate exercises. A combined stack is used only after its exact versions and bridge are qualified.
Does this provide ISO 26262 or SOTIF certification?+
No. The course teaches scope and evidence literacy. A RoboEdify completion credential is not an external standards certification.
Can a beginner join directly?+
The advanced core requires programming, robotics integration and estimation/control readiness. Preparation is assigned after a diagnostic.
Will I build every perception model from scratch?+
No. Supplied baselines support integration and evaluation. The focus is correct interfaces, failure analysis and a defensible driving-function assessment.
How is the learning workload structured?+
The program combines live mentor-led classes, guided labs, project work and support sessions. An advisor can explain the current class format before enrollment.
Can I study online or on campus?+
RoboEdify offers its Hyderabad campus, live online classes and a working-professional format. Course-specific compute and any physical-lab access are explained separately.
What if I need to pause or catch up?+
You can freeze your seat for up to 90 days and rejoin the next class at no extra fee. Saturday catch-up sessions and recordings of every live session support your learning. Recordings remain available for the lifetime of your account.
Is placement guaranteed?+
No. Support includes portfolio/profile preparation, interview practice and role-fit introductions where available. RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.
What happens if I miss a practical requirement?+
Feedback identifies the missing capability and reassessment work. Attendance alone does not meet the completion standard. Confirm the course-specific reassessment arrangements before enrollment.
How do I learn about fees and lab requirements?+
Ask a course advisor about preparation, the current offering, fees, computer or equipment access and support terms before enrollment.

Still have a question?

Find your starting point in Autonomous Driving & Self-Driving Systems Engineer.
One million AI-native professionals by 2027.

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Plan your learning

Course
Autonomous Driving & Self-Driving Systems Engineer
Preparation
Diagnostic-based preparation before the common core
Level
Specialist
Curriculum
12 modules

Confirm your intake dates, delivery mode, fees, assessment and practical access with RoboEdify before enrolling. Course content describes the learning scope; an enquiry does not reserve a seat.