Driving Perfomance

Vehicle Electronics

Courses

1. ADAS & Autonomous Driving Systems

Course Overview

This course provides a comprehensive understanding of Advanced Driver Assistance Systems and the foundations of autonomous driving. It covers sensing technologies, perception, decision-making, control, simulation, and validation strategies used in Level 1–4 autonomous systems.

Course Syllabus

Module 1: Introduction to ADAS & Autonomous Driving Levels
Module 2: Sensors for ADAS

  • Camera
  • Radar
  • LiDAR
  • Ultrasonic

Module 3: Perception Systems

  • Object detection
  • Lane detection
  • Traffic sign recognition

Module 4: Localization & Mapping

  • GPS/GNSS
  • SLAM
  • HD Maps

Module 5: Path Planning & Decision Making

  • Behavior planning
  • Trajectory generation

Module 6: Vehicle Control Systems

  • Lateral and longitudinal control

Module 7: Hardware & Software Architecture

  • ECUs
  • Compute platforms

Module 8: Simulation & Validation Tools

  • CARLA, PreScan, dSPACE

Module 9: Project: Build an ADAS Prototype Pipeline

Outcomes
  • Strong understanding of perception, planning, and control for ADAS
  • Ability to develop and test ADAS/autonomous driving algorithms
  • Readiness for roles in ADAS development, autonomous systems, and simulation engineering
  • Competitive advantage for AI-in-automotive roles

2. Sensor Fusion & Perception Algorithms

Course Overview

This course focuses on the algorithmic foundations of perception systems using multi-sensor data (camera, LiDAR, radar). It covers fusion techniques, computer vision, deep learning, and signal processing for autonomous vehicles and robotics.

Course Syllabus

Module 1: Introduction to Perception Systems
Module 2: Sensor Physics & Characteristics

  • Camera calibration
  • Radar signal fundamentals
  • LiDAR point clouds

Module 3: Computer Vision Algorithms

  • Feature detection
  • Optical flow
  • Segmentation

Module 4: Deep Learning for Perception

  • CNNs, object detection (YOLO, Faster R-CNN)
  • Semantic & instance segmentation

Module 5: Sensor Fusion Techniques

  • Kalman filters
  • Extended/Unscented KF
  • Particle Filters
  • Multi-sensor fusion pipelines

Module 6: 3D Perception

  • Point cloud processing
  • Object tracking

Module 7: Localization & Mapping Basics
Module 8: Project: Multi-sensor Fusion Perception Model

Outcomes
  • Ability to build perception models using camera, LiDAR, and radar data
  • Knowledge of modern deep learning and CV algorithms
  • Hands-on experience in multi-sensor fusion pipelines
  • Prepared for roles in perception engineering and autonomous systems development

3. In-Vehicle Infotainment (IVI) Systems Development

Course Overview

This course trains engineers to design, develop, and integrate IVI systems, covering multimedia frameworks, HMI development, Android Automotive OS, connectivity protocols, and ECU integration.

Course Syllabus

Module 1: Introduction to IVI Architecture
Module 2: Android Automotive / GENIVI / QNX Basics
Module 3: HMI/UX Development

  • UI frameworks
  • Voice assistants

Module 4: Multimedia Systems

  • Audio pipeline
  • Video playback
  • Media services

Module 5: Connectivity & Interfaces

  • Bluetooth, Wi-Fi, CarPlay, Android Auto
  • Vehicle data communication (CAN, Ethernet)

Module 6: Navigation Systems & GNSS Integration
Module 7: Security & Over-The-Air (OTA) Updates
Module 8: Testing & Validation of IVI Systems
Module 9: Project: Custom IVI Application or HMI

Outcomes
  • Ability to develop and integrate IVI applications
  • Strong understanding of multimedia, UI design, and connectivity systems
  • Competence in Android Automotive OS and related frameworks
  • Prepared for roles in IVI development, HMI engineering, and infotainment integration

4. Automotive Communication Protocols (CAN, LIN, FlexRay, Ethernet)

Course Overview

This course provides hands-on expertise in automotive communication networks essential for ECUs to communicate. It covers protocol fundamentals, message framing, diagnostics, network design, and tools used for testing and validation.

Course Syllabus

Module 1: Introduction to In-Vehicle Networks
Module 2: CAN Protocol

  • Frames, arbitration, error handling
  • CAN FD
  • Tools (CANoe, CANalyzer)

Module 3: LIN Protocol

  • Frame scheduling
  • Signal mapping
  • Master-slave configuration

Module 4: FlexRay Protocol

  • Static & dynamic segments
  • Time-triggered communication

Module 5: Automotive Ethernet

  • DoIP
  • SOME/IP
  • Time-Sensitive Networking (TSN)

Module 6: Diagnostics (UDS on CAN & Ethernet)
Module 7: Network Architecture & System Integration
Module 8: Project: End-to-End ECU Network Simulation

Outcomes
  • Ability to configure, debug, and validate CAN, LIN, FlexRay, and Ethernet networks
  • Understanding of diagnostics and service tools
  • Prepared for embedded networking, ECU integration, and validation roles
  • Advantage in autonomous and connected vehicle system development