View source for Robert bosch gmbh (20240208539). COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR THE BEHAVIOR PLANNING OF AN AT LEAST PARTIALLY AUTOMATED EGO VEHICLE simplified abstract

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


==Organization Name==

[[:Category:robert bosch gmbh|robert bosch gmbh]]


[[Category:robert bosch gmbh]]

==Inventor(s)==

[[:Category:Johannes Christian Mueller of Ostelsheim (DE)|Johannes Christian Mueller of Ostelsheim (DE)]][[Category:Johannes Christian Mueller of Ostelsheim (DE)]]

[[:Category:Anne Von Vietinghoff of Renningen (DE)|Anne Von Vietinghoff of Renningen (DE)]][[Category:Anne Von Vietinghoff of Renningen (DE)]]

[[:Category:Christian Heinzemann of Vellmar (DE)|Christian Heinzemann of Vellmar (DE)]][[Category:Christian Heinzemann of Vellmar (DE)]]

[[:Category:Heiko Freienstein of Weil Der Stadt (DE)|Heiko Freienstein of Weil Der Stadt (DE)]][[Category:Heiko Freienstein of Weil Der Stadt (DE)]]

[[:Category:Jens Oehlerking of Stuttgart (DE)|Jens Oehlerking of Stuttgart (DE)]][[Category:Jens Oehlerking of Stuttgart (DE)]]

[[:Category:Martin Butz of Stuttgart (DE)|Martin Butz of Stuttgart (DE)]][[Category:Martin Butz of Stuttgart (DE)]]

[[:Category:Martin Herrmann of Korntal (DE)|Martin Herrmann of Korntal (DE)]][[Category:Martin Herrmann of Korntal (DE)]]

[[:Category:Michael Rittel of Markgroeningen (DE)|Michael Rittel of Markgroeningen (DE)]][[Category:Michael Rittel of Markgroeningen (DE)]]

[[:Category:Ralf Kohlhaas of Calw (DE)|Ralf Kohlhaas of Calw (DE)]][[Category:Ralf Kohlhaas of Calw (DE)]]

[[:Category:Stefan Ruppin of Grafenau (DE)|Stefan Ruppin of Grafenau (DE)]][[Category:Stefan Ruppin of Grafenau (DE)]]

[[:Category:Steffen Knoop of Hohenwettersbach (DE)|Steffen Knoop of Hohenwettersbach (DE)]][[Category: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 20240208539 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 model to identify partial situations in the database
* Generating instances for each identified partial situation
* Analyzing all instances using evaluation models to determine possible behaviors
* Prioritizing behaviors based on boundary conditions and a rule set

Potential Applications:
- Autonomous driving systems
- Traffic management systems
- Fleet management solutions

Problems Solved:
- Enhancing safety in autonomous vehicles
- Improving decision-making processes in complex driving scenarios

Benefits:
- Increased efficiency in behavior planning
- Enhanced safety for passengers and other road users
- Optimized traffic flow and reduced congestion

Commercial Applications:
Title: "Advanced Behavior Planning System for Autonomous Vehicles"
This technology can be utilized by automotive companies, transportation agencies, and technology firms to develop advanced autonomous driving systems and traffic management solutions.

Questions about the technology:
1. How does this method improve the decision-making process of autonomous vehicles?
2. What are the key advantages of using pre-defined partial situations in behavior planning algorithms?


==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.



[[Category:B60W60/00]]
[[Category:CPC_B60W60/0015]]