Guardrail Estimation via Multi-Sensor Fusion for Lateral Control
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Solution Overview
Problem
Existing smart driving systems rely solely on cameras for lane line perception, leading to deteriorated system performance and stability, particularly in environments with poor lane line quality.
Innovation Solution
A guardrail estimation method based on multi-sensor data fusion, utilizing a combination of sensors such as cameras, millimeter-wave radars, and ultrasonic radars to determine guardrail information, which supplements lane line perception and enhances lateral control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only camera is used for lane line perception, then device complexity is reduced, but measurement precision and system stability deteriorate
Solution Approach 1:
The patent combines camera, millimeter-wave radar, and ultrasonic radar into a multi-sensor fusion system. The camera provides visual lane line detection, while the radars detect guardrails and road boundaries through electromagnetic waves and ultrasonic waves respectively. This merging of multiple sensing modalities compensates for the limitations of single-sensor systems, improving measurement precision without proportionally increasing system complexity.
Solution Approach 2:
The multi-sensor system performs multiple functions simultaneously: camera detects lane lines and guardrails visually, millimeter-wave radar detects guardrails and road boundaries through electromagnetic radiation, and ultrasonic radar detects nearby obstacles. This multi-functionality allows the system to maintain high measurement precision across different road conditions while managing device complexity through functional integration.
2Device complexity
If only camera is used for lane line perception, then device complexity is reduced, but system stability deteriorates
Solution Approach 1:
By merging camera, millimeter-wave radar, and ultrasonic radar data fusion, the system achieves more stable lateral control. The multi-sensor fusion algorithm integrates information from different sensing modalities, cross-validating detections and filtering out false positives. This redundancy and mutual verification mechanism significantly improves system reliability and stability in complex road environments.
Solution Approach 2:
The system implements feedback mechanisms where guardrail information from multiple sensors is continuously fed back to the lateral control module. The control system adjusts its operations based on real-time multi-sensor feedback, ensuring stable and reliable lateral control by adapting to changing road conditions and maintaining consistent control performance.
3Measurement precision
If multi-sensor data fusion is used, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent merges data from camera, millimeter-wave radar, and ultrasonic radar through a unified guardrail estimation algorithm. The system fuses multi-sensor data to accurately estimate guardrail positions, shapes, and distances, achieving high measurement precision. The integration architecture manages device complexity by establishing clear data flow relationships and processing hierarchies among the sensors.
Solution Approach 2:
The guardrail estimation process is segmented into distinct modules: camera-based lane line detection module, millimeter-wave radar-based guardrail detection module, ultrasonic radar-based obstacle detection module, and data fusion module. This segmentation allows each module to process specific sensor data independently before integrating results, managing computational complexity while maintaining high measurement precision through specialized processing.
Data Source
AI summary
A guardrail estimation method based on multi-sensor data fusion is provided. The guardrail estimation method comprises acquiring multi-sensor data and vehicle information, determining a traveling track of the vehicle based on the vehicle information, determining multiple guardrail sample points based on the multi-sensor data and the traveling track, and estimating guardrail information based on the multiple guardrail sample points. When the vehicle is controlled laterally, the guardrail information is used for correcting a lane line of poor quality; and when the lane line cannot be detected, the guardrail information is used for a lateral control downgrade processing, and the guardrail information also facilitates aided realization of fast and accurate expressway lane-level localization and realization of a higher-level aided driving function.