Vehicle Radar Tracking for Nearby Accident Reconstruction
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Solution Overview
Problem
Existing vehicular radar systems struggle to accurately detect and record nearby events, particularly accidents, leading to disputes over fault determination and inefficient accident reconstruction.
Innovation Solution
Equipping vehicles with radar sensors to collect and analyze radar data for detecting nearby objects, determining their trajectories, and storing relevant data for accident reconstruction, including metadata and sensor data from other sources like LiDAR and cameras, to facilitate accurate fault determination and data preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar sensors are used to detect nearby objects and predict collisions, then accident detection accuracy is improved, but false detections may occur
Solution Approach 1:
The patent combines radar sensor data with LiDAR sensor data and camera sensor data to create a multi-sensor fusion system. This merging of multiple sensing modalities allows for cross-validation of detected events, improving reliability by reducing false detections while maintaining high accident detection accuracy through correlated evidence from independent sensor sources.
Solution Approach 2:
The system implements feedback mechanisms where detected events are validated through multiple sensor inputs before triggering an accident detection alert. The radar system continuously monitors and adjusts its detection thresholds based on feedback from LiDAR and camera systems, reducing false positives while maintaining sensitivity to actual collision events.
2Measurement precision
If multiple sensor types (radar, LiDAR, camera) are integrated for comprehensive event tracking, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The patent designs a unified sensor system where radar, LiDAR, and camera sensors share common processing infrastructure and data fusion algorithms. This multi-functional architecture allows the same hardware platform to handle multiple sensor types, reducing overall system complexity while maintaining high measurement precision through comprehensive event tracking capabilities.
Solution Approach 2:
The system implements a nested architecture where radar data provides coarse-level detection, LiDAR data provides mid-level detail, and camera data provides fine-level verification. This nested structure organizes sensor inputs hierarchically, processing information from general to specific, which simplifies the integration complexity while achieving high measurement accuracy through progressive refinement.
3Loss of information
If radar data is continuously collected and stored for accident reconstruction, then data availability for fault determination is improved, but data storage requirements increase
Solution Approach 1:
The system performs preliminary filtering and event detection using radar data before full-scale data storage is activated. By pre-identifying potential accident scenarios through radar monitoring, the system can selectively store comprehensive multi-sensor data only when relevant events are detected, ensuring data completeness for accident reconstruction while minimizing unnecessary storage of normal operating data.
Solution Approach 2:
The patent extracts and stores only the critical portions of sensor data that are relevant to accident reconstruction, rather than storing all continuous sensor outputs. The system identifies and extracts key event parameters, metadata, and contextual information from the sensor streams, reducing storage requirements while maintaining data completeness for fault determination purposes.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of accident detection and reconstruction by using radar data to predict collisions and preserve relevant data, reducing false detections and improving fault determination through 3D tracking and weather-resistant sensing.
Implementation Method 1
Vehicular radar systems generally comprise sense and avoid techniques to sense any possible threats
Implementation Method 2
Radar sensors may be available for blind spot detection, lane change assistance, collision mitigation
Implementation Method 3
sensor data from other sources, such as LiDAR sensors and camera sensors
Data Source
AI summary
The present disclosure generally relates to methods, systems, apparatuses, and non-transitory computer readable media for incident detection using radar sensory data. A vehicular tracking system comprises at least one on-vehicle sensor, such as a radar sensor, that can perceive the environment around the vehicle and capture data related to possible incidents that may be viewed by the sensor. A radar sensor may provide radar data that can be used to calculate velocity vectors, accelerations vectors, azimuth and elevation angles of other vehicles, and this data may be collected and stored for possible incident characterization and accident investigation. The vehicle with the sensor may be configured to behave as a third-party witness to possible incidents. Several sensors may be used in conjunction with one another, where one sensor may trigger another sensor to begin capturing other data that the first sensor may be unable to capture.