Collaborative perception for self-driving trucks within the PRoPART project
Objective:
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.
BASELABS Contributions:
- 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.