A transferable belief model applied to LIDAR perception for autonomous vehicles
Light Detection and Ranging (LIDAR) sensors are commonly used in perception for autonomous vehicles because of their high accuracy, speed, and range. These characteristics make the sensor suitable for integration into the perception layer of controllers which have the capacity to avoid collisions with unpredicted obstacles. The objective of this work was to design a robust and efficient algorithm to acquire useful knowledge from LIDAR scans, and to test its performance in real road situations. The method is based on the construction of an Occupancy Grid based on the Transferable Belief Model, which has proved promising in other studies. Two clear advantages of this method are the reduction of the problem complexity in the phases of segmentation, occlusion detection and tracking, and its ease of integration with other sensors to allow the overall system to evolve towards reliable and complete perception.
vehicles tracking object detection laser ranging segmentation road safety