Sensor Technologies in Autonomous Vehicles

• An understanding of automotive systems and electronics
• Experience with programming languages such as Python or MATLAB
• Basic knowledge of control systems and signal processing

Audience

• Engineers working on autonomous vehicle development
• Automotive professionals interested in sensor integration
• IoT specialists exploring sensor applications in smart mobility

Sensors are fundamental to autonomous vehicle operation, enabling real-time perception, navigation, and decision-making.

This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers, automotive professionals, and IoT specialists who wish to understand the role of sensors in self-driving cars, covering LiDAR, radar, cameras, and sensor fusion techniques.

By the end of this training, participants will be able to:

• Understand the different types of sensors used in autonomous vehicles.
• Analyze sensor data for real-time vehicle perception and decision-making.
• Implement sensor fusion techniques to improve vehicle accuracy and safety.
• Optimize sensor placement and calibration for enhanced autonomous driving performance.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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