18598053. COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR PREDICTING TRAJECTORIES OF PARTICIPANTS IN A TRAFFIC SCENE simplified abstract (Robert Bosch GmbH)
Contents
- 1 COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR PREDICTING TRAJECTORIES OF PARTICIPANTS IN A TRAFFIC SCENE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR PREDICTING TRAJECTORIES OF PARTICIPANTS IN A TRAFFIC SCENE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Traffic Scene Prediction
- 1.13 Original Abstract Submitted
COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR PREDICTING TRAJECTORIES OF PARTICIPANTS IN A TRAFFIC SCENE
Organization Name
Inventor(s)
Marcel Hallgarten of Ehningen (DE)
COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR PREDICTING TRAJECTORIES OF PARTICIPANTS IN A TRAFFIC SCENE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18598053 titled 'COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR PREDICTING TRAJECTORIES OF PARTICIPANTS IN A TRAFFIC SCENE
Simplified Explanation
This patent application describes a computer-implemented method for predicting the trajectory of a participant in a traffic scene using a pretrained AI prediction model.
Key Features and Innovation
- Generation of a scene representation based on aggregated scene-specific information.
- Prediction of participant trajectories using the scene representation.
- Determination of the current position and track section of the participant.
- Transformation of the scene representation into a Frenet coordinate system.
- Prediction based on the resulting Frenet representation of the traffic scene.
Potential Applications
This technology can be applied in autonomous vehicles, traffic management systems, and road safety analysis.
Problems Solved
This technology addresses the challenge of accurately predicting participant trajectories in complex traffic scenes.
Benefits
- Improved accuracy in predicting participant trajectories.
- Enhanced safety and efficiency in traffic management.
- Potential for reducing accidents and congestion on roads.
Commercial Applications
- Autonomous vehicle navigation systems.
- Traffic flow optimization software.
- Road safety analysis tools.
Prior Art
Readers can explore prior art related to trajectory prediction in traffic scenes in the fields of artificial intelligence, computer vision, and transportation engineering.
Frequently Updated Research
Researchers are continually improving AI prediction models for better accuracy in predicting participant trajectories in dynamic traffic environments.
Questions about Traffic Scene Prediction
How does this technology contribute to road safety?
This technology enhances road safety by providing accurate predictions of participant trajectories, allowing for proactive measures to prevent accidents.
What are the potential limitations of using AI prediction models in traffic scene analysis?
AI prediction models may face challenges in accurately predicting trajectories in highly unpredictable traffic scenarios, requiring ongoing refinement and adaptation.
Original Abstract Submitted
A computer-implemented method for predicting at least one trajectory of at least one participant of a traffic scene. A scene representation of the traffic scene is generated on the basis of aggregated scene-specific information, and at least one trajectory for the at least one participant is predicted on the basis of the scene representation using a pretrained AI prediction model. A current position of the participant and a current track section on which the participant is currently located are determined. The scene representation is then transformed into at least one Frenet coordinate system, wherein the current track section specifies at least one section of the respective reference path for the Frenet transformation. The prediction is based on the at least one resulting Frenet representation of the traffic scene.
