Skill development program
Advanced Driver Assistance Systems (ADAS): Perception Training
A six-month program from TiHAN-IITH and CADFEM on how intelligent vehicles perceive, interpret, and respond to the road — built around sensor fundamentals, simulation, and real-world validation.
Overview
How a vehicle learns to read the road.
This TiHAN-IITH and CADFEM skill development program is built for learners who want to understand how intelligent vehicles perceive, interpret, and respond to real-world environments. It pairs core concepts with hands-on, simulation-driven workflows, giving participants practical exposure to sensor technologies and system validation — along with industry-relevant insight into how ADAS systems are designed, tested, and optimised using advanced simulation.
Course curriculum
Three phases, one build.
PHASE 01 — TiHAN-IITH
Foundations of perception
Core concepts behind how a vehicle senses and builds a picture of its surroundings — the groundwork for everything that follows.
Introduction to AD/ADAS technologies
- Definitions and levels of autonomy (L0–L5)
- Evolution and trends
- Use cases and applications
- Sensors, actuators, and control systems
- Perception, planning, and control loop in AD/ADAS
- Connected vehicles
AI/ML perception systems
- Sensor technologies: LiDAR, radar, ultrasonic, and camera systems
- Sensor fusion and data processing — advantages and limitations of each sensor type
- AI/ML object detection and classification: pedestrian, vehicle, and obstacle detection
- Tracking algorithms and 3D environment mapping
- Sensor calibration and synchronization
AD/ADAS system implementation
- Software and hardware integration: embedded systems and real-time processing
- ROS (Robot Operating System) and middleware
- Functional safety (ISO 26262)
- Test scenarios for AD/ADAS systems, including digital twins for testing autonomous vehicles
- Simulation and real-world testing tools and platforms (CARLA, PreScan)
- Road testing and safety standards (NCAP, Euro NCAP)
- Regulatory compliance and certification
PHASE 02 — CADFEM
Simulation-driven workflows
Hands-on, simulation-based training across sensor technologies, applying what's learned to realistic ADAS scenarios.
AVxcelerate interface, simulation pipeline & asset preparation
- AVxcelerate project setup and Sensor Labs quick start
- Scene building and traffic actor assignment
Sensor simulation — physically accurate sensor output
- Camera modeling and data logging (images, raw data)
- Radar modeling and data logging (raw, RDM, PCD, visualisation and post-processing)
- LiDAR modeling (rotating) and PCD data visualisation
- Generating sensor data across scenarios — city, highway, country — and day, night, and weather variations
Open-loop simulation with an open-source perception algorithm (YOLO)
- Real-time detection of various objects and assets
- Output of object details as YOLO output
- Sensor fusion study: camera + radar fusion simulation
Closed-loop simulation
- Software-in-loop simulation — studying vehicle data and dynamics
- Motion control and planning modeled in MBSE tools (SCADE)
- Driving simulators and scenario generation, from OSM data to real-world scenarios
- Regulation requirements and scenario preparation
- Closed-loop simulation for an L1/L2 ADAS function (e.g. AEB)
Python API integration and dataset generation
- Python API workflow for automated data collection
- Labelling and dataset preparation for AI perception
- Hands-on training exercise
Assessment
PHASE 03 — Joint (TiHAN-IITH and CADFEM)
Applied validation
TiHAN-IITH and CADFEM together, working through real-world datasets and autonomous vehicle validation workflows.
What this phase covers
- Joint sessions led by TiHAN-IITH and CADFEM together
- Hands-on work with real-world datasets
- Autonomous vehicle validation workflows
Detailed syllabus for this phase will be shared closer to the start date.
Who should attend
Built for people who build vehicles.
Students
Pursuing Engineering (Bachelor's or Diploma) or a Master's degree.
Professionals
Working in software development & testing (systems), hardware design & validation, product design, technical support, communication, or network engineering.
Faculty & researchers
Faculty members and postdoctoral researchers with a relevant technical background.
Focused industries
Key highlights
- Multi-phase structured learning
- Industry-relevant simulation training
- Exposure to autonomous vehicle validation workflows
- Hands-on experience with real-world datasets
Enrol