Sensor Grouping Detection for Extended Driving Situation Range
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Solution Overview
Problem
Current sensor technologies have limited detection ranges, making it difficult to detect driving-relevant situations, such as traffic jams or wrong-way drivers, at sufficient distances for timely reaction, especially in high-speed scenarios, due to insufficient resolution of measurement data representation.
Innovation Solution
The method analyzes measurement data for groupings of objects, such as vehicles, road boundaries, and lane markings, to detect relevant situations at larger distances by recognizing patterns and changes in size, density, and relative speed, allowing for earlier detection and warning systems to be activated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual objects are detected using fixed pixel/voxel discretization, then detection precision is maintained, but detection distance is limited
Solution Approach 1:
The patent merges multiple individual objects (vehicles, road boundaries, lane markings) into a unified grouping structure. By combining discrete object detections into a cohesive grouping that spans multiple pixels/voxels, the system achieves detection at larger distances while preserving the precision of individual object recognition through the structured representation of spatial relationships within the grouping.
Solution Approach 2:
The patent transitions from detecting individual objects in two-dimensional image space to detecting groupings that incorporate third-dimensional spatial relationships. By representing groupings with structured data that captures relative positions, orientations, and distances between multiple objects, the system extends detection capability beyond the limitations of fixed pixel discretization in conventional 2D space.
2Length of stationary object
If multiple sensors with different detection ranges are combined, then detection range is extended, but device complexity increases
Solution Approach 1:
The patent implements dynamic adaptation of detection strategies based on distance and context. The system adjusts its analysis focus between individual objects and groupings depending on the detected scenario, allowing a single sensor system to effectively cover multiple detection ranges by dynamically changing its processing mode rather than relying on multiple fixed sensors.
Solution Approach 2:
The patent makes the sensor system universal by enabling it to perform both individual object detection and grouping detection with the same hardware. The classification module is designed to handle multiple detection tasks (single objects, groups of objects, traffic situations) using a unified approach, eliminating the need for specialized sensors for different detection ranges.
3Length of stationary object
If grouping detection is used to extend detection distance, then detection distance increases, but measurement precision may be reduced
Solution Approach 1:
The patent performs preliminary detection of individual objects within the grouping at their native resolution before synthesizing the grouping representation. By first identifying individual vehicles, road boundaries, and lane markings with full precision, then combining them into a grouping structure, the system preserves measurement precision from the individual object level while achieving extended detection distance through the aggregated grouping signal.
Data Source
AI summary
A method for detecting a relevant region in the surroundings of an ego vehicle, in which a situation exists which is relevant to the driving and/or safety of the ego vehicle, from measurement data of a sensor which observes at least a portion of the surroundings, the measurement data being discretized into pixels or voxels and/or are suitably represented in some other way, the existence of the relevant situation being dependent on the presence of at least one characteristic object in the surroundings, and the resolution of the pixels, voxels and/or the other representation being insufficient for directly detecting the characteristic object, the measurement data being analyzed for the presence of a grouping of objects which contains the characteristic object, the resolution of the pixels, voxels and/or the other representation being sufficient for detecting the grouping. A region in which the grouping is detected is classified as a relevant region.