The project is focused on decision making, planning and control for automated vehicles and excludes sensor-data fusion.
- Algorithms and data models applied in real automated driving system for motion planning and control
- Mechanisms for safe interaction with other CAVs, infrastructure, traffic management, interactions with human-driven vehicles, bicyclists, pedestrians
- Evaluation in context of overall traffic system
- Software quality, reliability and TRL as required for research projects and prototypes
Concrete features :
- High-definition road-map representation and loading from OpenDrive file
- Planning modules for smooth "in-lane" driving, (cooperative) lane changes, emergency maneuvers, parking, navigation
- Trajectory tracking module for stabilization of vehicle
- Data models for automation-internal scene understanding, environment models
- Data abstraction views for decoupling of planning algorithms and environment models
- Vehicle model for simulation
- Object detection model (in simulation it replaces the sensor-data fusion pipeline, which is not covered by this project)
- V2X communication model for simulation, (high level, based on look-up tables, no detailed network simulation)
- Interface for co-simulation with Eclipse SUMO: Simulation of traffic and infrastructure around automated vehicle
- Interface to ROS (Robot Operating System) and possibly other middle-ware interfaces
- Modularity and system independence
Out of scope, not considered:
- Sensor data fusion algorithms for automated driving are currently not covered. The main focus is decision making, not perception. The provided decision making and control algorithms can be evaluated together with sensor/perception models in simulation.
- The project cannot and will not promise creation of highly reliable code, which could be applied in products. The focus is rather on flexible code useful for prototyping and research.
- Non-road-based autonomous systems are not considered.