20240037296. COMPARISON OF DIGITAL REPRESENTATIONS OF DRIVING SITUATIONS OF A VEHICLE simplified abstract (Robert Bosch GmbH)

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COMPARISON OF DIGITAL REPRESENTATIONS OF DRIVING SITUATIONS OF A VEHICLE

Organization Name

Robert Bosch GmbH

Inventor(s)

Patrick Weber of Rutesheim (DE)

COMPARISON OF DIGITAL REPRESENTATIONS OF DRIVING SITUATIONS OF A VEHICLE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240037296 titled 'COMPARISON OF DIGITAL REPRESENTATIONS OF DRIVING SITUATIONS OF A VEHICLE

Simplified Explanation

The abstract describes a method for comparing two digital representations of driving situations of a vehicle. The digital representations include information about the occupancy of the environment of the vehicle by traffic-relevant objects. The method involves subdividing the environment into a grid of subregions and determining the occupancies of the subregions based on the occupancy information in the digital representations. These occupancies are combined to form a fingerprint of each driving situation. A similarity measure is then calculated between the two fingerprints according to a predefined dimensional rule. If the similarity measure satisfies the predefined rule, it is determined that the two driving situations are identical or at least similar.

  • The method compares digital representations of driving situations by analyzing the occupancy information of traffic-relevant objects in the environment of a vehicle.
  • The environment is divided into subregions and the occupancies of these subregions are determined based on the occupancy information in the digital representations.
  • The occupancies of the subregions are combined to form a unique fingerprint for each driving situation.
  • A similarity measure is calculated between the fingerprints of the two driving situations.
  • If the similarity measure satisfies a predefined rule, it is determined that the driving situations are identical or similar.

Potential applications of this technology:

  • Autonomous vehicles: The method can be used to compare and analyze different driving situations to improve the decision-making process of autonomous vehicles.
  • Traffic management: The technology can be applied to analyze and compare traffic situations in real-time, allowing for more efficient traffic management and congestion reduction.
  • Driver assistance systems: The method can be utilized in driver assistance systems to compare and evaluate different driving situations, providing warnings or assistance to the driver when necessary.

Problems solved by this technology:

  • Comparison of driving situations: The method provides a systematic way to compare and analyze different driving situations, allowing for better understanding and decision-making in various applications.
  • Identifying similar driving situations: The technology enables the identification of driving situations that are similar or identical, which can be useful for various purposes such as accident reconstruction or traffic pattern analysis.

Benefits of this technology:

  • Improved safety: By comparing and analyzing driving situations, the technology can contribute to the development of safer driving systems and assist in accident prevention.
  • Enhanced efficiency: The method allows for the efficient comparison and evaluation of driving situations, leading to improved traffic management and optimized decision-making in autonomous vehicles.
  • Better understanding of traffic patterns: By analyzing and comparing driving situations, the technology can provide valuable insights into traffic patterns and behavior, aiding in the development of more effective transportation systems.


Original Abstract Submitted

a method for comparing two digital representations of driving situations of a vehicle. the digital representations include occupancy information about an occupancy of the environment of the vehicle by traffic-relevant objects. the method includes: subdividing a region of the environment of the vehicle into a grid of subregions; ascertaining, based on the occupancy information in the first digital representation, the occupancies of the subregions by traffic-relevant objects, and combining them to form a fingerprint of the first driving situation; ascertaining, based on the occupancy information in the second digital representation, the occupancies of the subregions by traffic-relevant objects, and combining them to form a fingerprint of the second driving situation; ascertaining a similarity measure between the first fingerprint and the second fingerprint according to a predefined dimensional rule; determining that the two driving situations are identical or at least similar if the similarity measure satisfies the similarity measure.

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