Collaborative perception for self-driving trucks within the PRoPART project
Highly accurate positioning solution for automotive use, taking advantage of the excellent characteristics of Galileo signals and combining them with other positioning and sensor technologies.
- Creation of an environment model with collaborative perception, processing the sensor data from onboard sensors and V2X data from the infrastructure sensors. The environment model was created by implementing both a dynamic object fusion and an occupancy grid.
- Developing of a Situation Assessment Module, which decides whether a lane-change maneuver is possible and safe. The decision making combined the results made by two independent paths: a collision detector and a check of occupancy in the neighboring area of the truck.
For more information visit the project website.