CADFEM x TiHAN IITH
Advanced Driver Assistance Systems (ADAS): Perception Training
Overview
The TiHAN-IIT Hyderabad Skill Development Program on Advanced Driver Assistance Systems (ADAS) – Perception, a collaboration between CADFEM and TiHAN-IITH, is designed to equip learners with the skills needed to understand how intelligent vehicles perceive, interpret, and respond to real-world environments. Combining core concepts with hands-on, simulation-driven workflows, the program offers practical exposure to sensor technologies and system validation. Participants gain industry-relevant insights into how ADAS systems are designed, tested, and optimized using advanced simulation.
Course curriculum
Phase 1 (TiHAN‑IITH)
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.
- 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 Calibrations and Synchronization
- 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: Use of Digital Twins in Testing Autonomous Vehicles, Simulation and Real-World Testing, Simulation Tools and Platforms (CARLA, PreScan), Road Testing and Safety Standards (NCAP, EuroNCAP), Regulatory Compliance and Certificatio
Phase 2 (CADFEM)
AVxcelerate Interface, Simulation Pipeline & Asset Preparation
- AVxcelerate project setup, Sensor Labs quick star
- Scene building and traffic actor assignment
Sensor Simulation
Physically Accurate Sensor Output:
- Camera modeling and data log - images, raw data
- Radar modeling and data log - raw, rdm, pcd, visualisation/ postprocessing
- LiDAR modeling (Rotating) - visualise PCD dat
Generate sensor data for:
- Various scenarios - city, Highway, Country etc.
- Day, Night, Weather Variation
Open-loop simulation with open-source Perception algorithm (YOLO)
- Simulate in real-time detecting various objects and assets
- Output object details as YOLO output.
- Sensor fusion study: Camera + Radar fusion simulation
Learn Software-in-loop simulation
- Study each block of tool chain namely vehicle data and dynamics.
- Study Motion Control and planning and modeling in MBSE tools namely SCADE.
- Study simulation preparation from OSM data to real-world scenario
- Study Regulation requirements and scenario preparation
- Simulate closed-loop simulation for L1/L2 ADAS function (ex. AEB)
- Python API workflow for automated data collection
- Labelling and dataset preparation for AI perception
- Hands on Training (Exercise)
Phase 3 (TiHAN-IITH and CADFEM)
- Capstone Project
Who Should Attend
- Students pursuing Engineering (Bachelors/Diploma) or Masters.
- If you are a professional in technical areas such as Software Development & Testing (System), Hardware Design & Validation, Product Design, Technical Support, Communication, and Network Engineering, you should consider applying to this program
- 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