Perception system for autonomous vehicles

The goal of this project was to develop a perception system for autonomous vehicles capable of accurately sensing and interpreting the vehicle’s surroundings.

The development process included creating algorithms for sensor fusion, object detection, and environmental mapping.

The technologies and tools used included Lidar, radar, cameras, and machine learning frameworks such as TensorFlow and PyTorch. Additionally, automotive-specific processors were used to ensure the high computational power and real-time processing required for such a system.

The main tasks included developing algorithms for sensor fusion, which combined data from LiDAR, radar, and cameras to create a comprehensive and accurate representation of the vehicle’s surroundings. These algorithms were further developed to reliably detect objects and track their movements. Another key aspect was environmental mapping, where dynamic maps were created to capture and depict the real-time position of the vehicle and its surroundings.

The development process included extensive simulations and real-world tests to validate the accuracy and reliability of the perception system. These tests were conducted in both virtual environments and on test tracks, as well as in real-world traffic, to assess and optimize the system’s performance under various conditions.

The end result was a robust perception system that significantly enhanced the safety and functionality of autonomous vehicles. Sensified was responsible for executing this project and ensured the development of a powerful and reliable system through the use of advanced technologies and rigorous testing methods, meeting the high demands of the autonomous vehicle industry.