Sensor Bearing Prediction for Denser Vehicle Point Clouds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sensor technologies produce sparse point clouds with measurement uncertainties, limiting their effectiveness in applications requiring dense data for accurate processing, particularly in AI modules.
Innovation Solution
A method that predicts sensor data by using an object motion model to virtually shift measurements from preliminary times to a target time, combining actual and predicted relative bearings to create denser point clouds, enhancing data density for improved processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sensor measurements are taken at discrete time points, then measurement simplicity is maintained, but data density is insufficient for accurate AI processing
Solution Approach 1:
The system performs preliminary measurements at multiple time points before the target time, then uses motion models to predict what the measurement would have been at the target time. This preliminary action of measuring and predicting allows the system to create denser point clouds without requiring continuous measurement at the exact target moment.
Solution Approach 2:
The system creates virtual copies of preliminary measurements by predicting their values at the target time using motion models. These predicted measurements are copies that estimate where objects would be at the target time based on their motion trajectories, effectively multiplying the data points available for processing.
2Quantity of substance
If multiple preliminary measurements are taken before target time, then data density increases, but processing time increases
Solution Approach 1:
The system replaces the mechanical approach of taking multiple actual measurements at the target time with a computational approach using motion models. Instead of physically measuring multiple times, the system uses mathematical predictions based on object motion models to generate the equivalent information, significantly reducing processing time.
Solution Approach 2:
The system changes the temporal parameter of measurements by predicting what values would be at the target time based on preliminary measurements. This parameter transformation allows the system to work with a single target time while incorporating information from multiple preliminary times, maintaining data density without the time cost of multiple simultaneous measurements.
3Measurement precision
If measurements are referenced to sensor null point, then coordinate consistency is maintained, but measurement uncertainty increases due to object motion
Solution Approach 1:
The system dynamically adjusts measurements by predicting object positions at the target time based on their motion. Instead of using static measurements that may be outdated due to object motion, the system applies dynamic corrections using motion models to estimate where objects are at the target time, maintaining both coordinate consistency and measurement accuracy.
Solution Approach 2:
The system uses feedback from motion models to correct and refine measurements. By continuously updating predictions based on object motion patterns and comparing them with actual measurements, the system compensates for uncertainties introduced by object motion while maintaining reference to the sensor null point coordinate system.
Data Source
AI summary
A method for producing sensor data involves a sensor detecting a relative bearing of an object at a sensor null point as a preliminary sensor bearing at preliminary times, and a target sensor bearing at a target time, and a computing module predicting, based on an object motion model, a respective prediction sensor bearing at the target time. The sensor data is formed as a set comprising the target sensor bearing and the prediction sensor bearing at the target time. Another method for controlling a motor vehicle in surroundings having the object involves producing the sensor data with regard to the object and triggering an actuator with regard to the object based on the sensor data. A sensor module contains the sensor, the computing module, and an output for the sensor data. A motor vehicle contains the sensor module.