Traffic Object Identification With Directional Clustering for Guardrails
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Solution Overview
Problem
Advanced driver assistance systems face challenges in reliably identifying objects in traffic space, particularly guardrails, due to high reflection distances and adverse angles, leading to incomplete shape approximation and potential false indication of free space where obstacles exist.
Innovation Solution
A method that transforms detected positions using directional scaling to create non-circular clustering regions, allowing broader gaps in one direction than another, effectively connecting guardrail detections along the vehicle's path while avoiding artificial obstacle creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the clustering radius is increased to connect distributed guardrail detections, then the completeness of guardrail shape approximation is improved, but gaps between separate objects (e.g., left and right guardrails) are incorrectly closed creating artificial obstacles
Solution Approach 1:
The patent applies different clustering radii to different spatial locations and directions. Specifically, it uses an anisotropic clustering approach where the clustering radius varies depending on the direction from the seed point, allowing broader connection in directions parallel to the road (to connect guardrail segments) while maintaining stricter criteria in directions perpendicular to the road (to avoid merging separate guardrails). This local differentiation of clustering quality resolves the contradiction by adapting the clustering behavior to the local geometric context of each detection point.
Solution Approach 2:
The patent introduces asymmetry in the clustering regions by using elliptical or directionally-biased clustering zones instead of circular symmetric regions. The clustering radius is extended preferentially in the longitudinal direction (parallel to expected guardrail orientation) while being constrained in the transverse direction. This asymmetric clustering shape allows the system to connect distributed detections along a guardrail without incorrectly merging separate guardrails that are laterally separated, thus resolving the contradiction between completeness and gap preservation.
2Measurement precision
If the clustering radius is kept small to preserve actual gaps between objects, then false indication of free space is reduced, but guardrail detections at high distances are not properly connected resulting in incomplete shape approximation
Solution Approach 1:
The patent implements dynamic clustering radii that adapt based on the local density and spatial distribution of detections. Rather than using a fixed clustering radius, the system adjusts the effective clustering radius for each seed point based on the geometric relationships between detections and the expected object structure. This dynamic adjustment allows the system to use larger effective radii for connecting distant guardrail segments while maintaining smaller effective radii for preserving genuine gaps between separate objects, thus resolving the contradiction between shape completeness and gap precision.
Data Source
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AI summary
A method for identifying objects in a traffic space by means of a sensor system arranged at or in a host vehicle comprises the steps of determining the positions of a plurality of object surface spots detected by the sensor system in a vehicle coordinate system, assigning the determined positions of object surface spots to at least one detection group by means of a clustering algorithm, wherein the clustering algorithm uses clustering regions defining threshold distances, and identifying at least one object by determining that positions of object surface spots belonging to the same detection group form part of a single object. The determined positions of object surface spots are transformed in a transformation process and the clustering algorithm is applied to the transformed positions of object surface spots. The transformation process includes a directional scaling step that renders the clustering regions non-circular in the vehicle coordinate system.