1. Simulation Environment

1.3 Behavior of other Road Users

Collision avoidance includes the detection of roadway boundaries that are not always visible (structures in the water, shallow shore areas, etc.) and the detection of other road users and their movement behavior. The behavior of other road users must be continuously observed and continuously predicted. Especially in narrow channels without avoidance possibilities, a corresponding accuracy is required.

Based on simulation data (or, in the case of the real ship, via the visual sensor system), the recognition and identification of other road users is performed by object recognition and tracking algorithms. In the same way, the own behavior is monitored and predicted, also in order to establish a further level of protection in this way.

Based on the sensor information, classification methods are used in position and situation recognition as well as behavior generation, as well as for the recognition of other ships as well as other objects as well as their intention. The man-machine interface to be developed visualizes known position values of all traffic participants as well as the intended movements of the own and the precalculated movements of other ships.

Work packages

The simulation environment as a virtual image of the real world is the central development environment for all control and monitoring algorithms. A digital twin of the test vessel is implemented in the simulation environment.

The development of the automated control of the inland vessel will initially take place in a simulator. In the first work package, the existing simulator will therefore be extended so that sensor readings can be generated virtually. The artificial intelligence (AI) developed in parallel in a modular fashion for ship control and for implementing the driving behavior functions will then be integrated in a simulation model in such a way that a later parallel porting to a real inland waterway vessel will be possible. Furthermore, the behavior of other road users (vessels in the vicinity of the automated barge) will be modeled and integrated into the simulation environment. The development of the AI for the implementation of the driving behavior functions for different tasks is initially carried out in parallel and then tested in the simulator. Fundamental questions about the choice and structure of the module are efficiently addressed in this way.

1.1 Simulator

The virtual home of the digital twin

1.2 AI / Behavior Control

The central algorithms: ship guidance and driving behavior functions

1.3 Behavior of other Road Users

Prerequisite for accident-free traffic: understanding the traffic situation

To test the test vessel, test scenarios are first defined. Subsequently, the automated vessel control is integrated and iteratively adapted. Test runs are absolutely necessary for this.

2.1 Sensors

The senses of AI

2.2 Actuator

Artificial intelligence takes the control

2.3 Human-Machine Interface

Development of the human-machine interface

The realization of the automation of the test vessel requires a fine conception and coordination of the algorithms. Furthermore, the automation functions must be tested in advance in the simulation environment. This is followed by validation on the real system.

3.1 Fine-Tuning and Tuning of the Algorithms

Fine conception and tuning of the algorithms for the automation of the inland vessel.

3.2 Implementation

Technical implementation of the automation functions

3.3 Integration of Autom. Vessel Guidance in the Simulation Environment

Integration of automated ship guidance in the simulation environment

3.4 Validation ML/KI and Vessel Control

Validation of the AI

Sensors and actuators will be used to equip the test vessel. A human-machine interface is also being developed.

4.1 Definition of Test Scenarios

Testing of the developments

4.2 Integration and Adaptation of Control in Test Vessel

Integration and Adaptation of Control in Test Vessel

4.3 Test Drives and Demonstration

From theory to practice

5.1 Profitability Analysis

Economic efficiency analysis

5.2 Rating

Final evaluation of automation

Tel.: 0203 – 99 36 9 – 51
E-Mail: autobin@dst-org.de

1. Simulation Environment

1.3 Behavior of other Road Users

1.3 Behavior of other Road Users

Work packages

1.1 Simulator

1.2 AI / Behavior Control

1.3 Behavior of other Road Users

2.1 Sensors

2.2 Actuator

2.3 Human-Machine Interface

3.1 Fine-Tuning and Tuning of the Algorithms

3.2 Implementation

3.3 Integration of Autom. Vessel Guidance in the Simulation Environment

3.4 Validation ML/KI and Vessel Control

4.1 Definition of Test Scenarios

4.2 Integration and Adaptation of Control in Test Vessel

4.3 Test Drives and Demonstration

5.1 Profitability Analysis

5.2 Rating

Projekt partners