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Course Outline

Introduction to Autonomous Vehicle Sensors

  • Overview of autonomous vehicle architecture.
  • The role of sensors in self-driving technology.
  • Challenges and limitations of sensor-based perception.

LiDAR Sensors in Autonomous Vehicles

  • How LiDAR works: principles and applications.
  • LiDAR data processing and 3D mapping.
  • Strengths and limitations of LiDAR in self-driving systems.

Radar and Ultrasonic Sensors

  • Radar for object detection and collision avoidance.
  • Interpreting radar signals and Doppler effects.
  • Ultrasonic sensors for low-speed navigation.

Camera and Computer Vision Systems

  • Types of cameras used in autonomous vehicles.
  • Image processing techniques for object recognition.
  • Deep learning applications in visual perception.

Sensor Fusion and Data Integration

  • Introduction to sensor fusion techniques.
  • Combining LiDAR, radar, and camera data for better accuracy.
  • Kalman filtering and deep learning approaches to sensor fusion.

Real-Time Processing and Autonomous Decision-Making

  • Latency and real-time constraints in autonomous perception.
  • Processing sensor data for navigation and obstacle avoidance.
  • Case studies: Tesla, Waymo, and other industry leaders.

Testing and Calibration of Autonomous Vehicle Sensors

  • Methods for sensor calibration and error correction.
  • Testing sensor performance in different environments.
  • Optimizing sensor placement for enhanced vehicle perception.

Future Trends in Autonomous Vehicle Sensing

  • Emerging sensor technologies in self-driving cars.
  • AI-driven advancements in sensor data analysis.
  • The future of fully autonomous vehicle perception systems.

Summary and Next Steps

Requirements

  • A solid understanding of automotive systems and electronics.
  • Experience with programming languages such as Python or MATLAB.
  • Foundational knowledge of control systems and signal processing.

Audience

  • Engineers involved in autonomous vehicle development.
  • Automotive professionals interested in sensor integration.
  • IoT specialists exploring sensor applications in smart mobility.
 21 Hours

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  • Format: Online (live), In-company (at your offices), or Hybrid.
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