Radar Reference Mapping Using HD Map Objects and Occupancy Grids

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Solution Overview

Problem

Current radar localization systems face challenges in generating accurate and complete radar reference maps, especially in environments with insufficient or low-quality radar localization objects, leading to increased driver takeovers and decreased safety and satisfaction in autonomous vehicle operations.

Innovation Solution

The method involves receiving a high-definition map, determining HD map objects, and indicating occupancy cells in a radar occupancy grid based on object attributes to generate a robust and spatially efficient radar reference map, which can be updated through multiple iterations using radar detections and HD map data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If radar reference maps are generated using insufficient or low-quality radar localization objects, then map generation is simpler and faster, but localization accuracy deteriorates leading to increased driver takeovers

Engineering Contradiction:
Improvemap generation speedVSAvoidlocalization accuracy
Core Design Contradiction:
ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary actions by pre-processing radar detections to identify and classify localization objects before map generation. It pre-computes object attributes such as positions, sizes, and types, and prepares a structured representation of the environment. This preliminary organization of data enables faster subsequent map generation while maintaining high localization accuracy through quality filtered input data.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The system changes parameters by dynamically adjusting the selection criteria for radar localization objects based on environmental conditions and map quality requirements. It modifies parameters such as object detection thresholds, classification criteria, and attribute precision levels to optimize the balance between map generation speed and localization accuracy for different operating scenarios.

Inventive Principle:
Principle #35Parameter changes

2Measurement precision

If complete and accurate radar reference maps are generated, then localization accuracy improves, but map generation complexity and processing time increase

Engineering Contradiction:
Improvelocalization accuracyVSAvoidmap generation complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the map generation process into distinct modular stages: radar detection and filtering, localization object identification, attribute extraction and validation, spatial relationship computation, and map structure construction. Each segment processes specific aspects of the data independently, enabling parallel processing and reducing overall complexity while maintaining comprehensive accuracy through systematic coverage of all necessary processing steps.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The system introduces intermediary data structures and processing layers between raw radar detections and the final reference map. It uses intermediate representations such as filtered object lists, validated attribute sets, and pre-computed spatial relationships to bridge the gap between complex raw data and the structured final map, simplifying the overall generation process while preserving accuracy.

Inventive Principle:
Principle #24Intermediary (Mediator)

3Reliability

If comprehensive HD map objects and attributes are processed, then reference map completeness improves, but processing time and computational resources increase

Engineering Contradiction:
Improvereference map completenessVSAvoidprocessing time
Core Design Contradiction:
ReliabilityVSLoss of time

Solution Approach 1:

The system applies local quality by processing HD map objects and attributes with varying levels of detail based on their importance and impact on localization accuracy. Critical objects such as road boundaries and intersections receive comprehensive processing with full attribute sets, while less critical objects use simplified representations. This selective processing approach ensures reference map completeness for essential features while reducing overall processing time through differential detail levels.

Inventive Principle:
Principle #3Local quality

Data Source

PatentEP4016129A1Radar reference map generation
Publication Date: 2022.06.22 APTIV TECHNOLOGIES AG
  • EP4016129A1 patent drawingFigure 1
  • EP4016129A1 patent drawingFigure 2-1
  • EP4016129A1 patent drawingFigure 2-2

AI summary

Methods and systems are described that enable radar reference map generation. A high-definition (HD) map is received and one or more HD map objects within the HD map are determined. Attributes of the respective HD map objects are determined, and, for each HD map object, one or more occupancy cells of a radar occupancy grid are indicated as occupied space based on the attributes of the respective HD map object. By doing so, a radar reference map may be generated without a vehicle traversing through an area corresponding to the radar reference map.