18530546. COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR THE BEHAVIOR PLANNING OF AN AT LEAST PARTIALLY AUTOMATED EGO VEHICLE simplified abstract (Robert Bosch GmbH)

COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR THE BEHAVIOR PLANNING OF AN AT LEAST PARTIALLY AUTOMATED EGO VEHICLE

Organization Name

Robert Bosch GmbH

Inventor(s)

Johannes Christian Mueller of Ostelsheim (DE)

Anne Von Vietinghoff of Renningen (DE)

Christian Heinzemann of Vellmar (DE)

Heiko Freienstein of Weil Der Stadt (DE)

Jens Oehlerking of Stuttgart (DE)

Martin Butz of Stuttgart (DE)

Martin Herrmann of Korntal (DE)

Michael Rittel of Markgroeningen (DE)

Ralf Kohlhaas of Calw (DE)

Stefan Ruppin of Grafenau (DE)

Steffen Knoop of Hohenwettersbach (DE)

COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR THE BEHAVIOR PLANNING OF AN AT LEAST PARTIALLY AUTOMATED EGO VEHICLE - A simplified explanation of the abstract

This abstract first appeared for US patent application 18530546 titled 'COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR THE BEHAVIOR PLANNING OF AN AT LEAST PARTIALLY AUTOMATED EGO VEHICLE

The abstract describes a computer-implemented method for behavior planning of an automated EGO vehicle using a database of pre-defined partial situations and an evaluation model.

• Aggregating situation-specific information
• Generating an environment model based on the information
• Analyzing the environment model to identify partial situations
• Generating instances for each identified partial situation
• Analyzing all instances using evaluation models to determine possible behaviors
• Prioritizing behaviors based on boundary conditions and rule set

Potential Applications: - Autonomous driving systems - Traffic management systems - Robotics

Problems Solved: - Efficient decision-making for automated vehicles - Improved safety on the roads - Enhanced traffic flow

Benefits: - Increased efficiency in handling complex driving scenarios - Enhanced safety for passengers and pedestrians - Reduced traffic congestion

Commercial Applications: Title: "Advanced Behavior Planning System for Autonomous Vehicles" This technology can be utilized by automotive companies to develop advanced autonomous driving systems for commercial use. It can also be integrated into traffic management solutions for smart cities, improving overall transportation efficiency.

Prior Art: Researchers in the field of autonomous vehicles have explored various methods for behavior planning, including machine learning algorithms and sensor fusion techniques. However, this specific approach of using pre-defined partial situations and evaluation models may offer a unique solution to the challenges in autonomous driving.

Frequently Updated Research: Researchers are continuously exploring new algorithms and technologies to enhance the behavior planning of autonomous vehicles. Stay updated on the latest advancements in this field to ensure optimal performance and safety in automated driving systems.

Questions about Behavior Planning for Autonomous Vehicles: 1. How does this method compare to other approaches in behavior planning for autonomous vehicles? This method stands out for its use of pre-defined partial situations and evaluation models, offering a structured approach to decision-making in complex driving scenarios.

2. What are the key factors influencing the prioritization of behaviors in this system? The prioritization is based on boundary conditions determined by the evaluation models and the rule set, ensuring that the EGO vehicle selects the most appropriate behavior in a given situation.

Original Abstract Submitted

A computer-implemented method for the behavior planning of an at least partially automated EGO vehicle. The method uses a database of pre-defined partial situations and an evaluation model for each partial situation, as well as a pre-defined rule set for evaluating possible behaviors of the EGO vehicle in a given situation. The EGO vehicle performs: aggregating situation-specific information; generating an environment model of the given situation based on the situation-specific information; analyzing the environment model to identify at least one partial situation in the database; generating at least one instance for each identified partial situation; analyzing all generated instances by using the evaluation model of the respectively underlying partial situation to determine boundary conditions for the possible behaviors of the EGO vehicle in the given situation; prioritizing the possible behaviors of the EGO vehicle based on the boundary conditions determined in this way in conjunction with the rule set.