Onboard Cluster Tracking for Motion-Distorted Sensor Spins
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately tracking objects due to occlusions and sensor measurement inaccuracies, which can impact driving decisions and route planning.
Innovation Solution
A method and system for tracking objects using a surface matching algorithm to associate clusters from consecutive sensor spins, adjust motion characteristics, and control the vehicle based on accurate motion data, addressing issues like under- and over-segmentation and motion distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sensor data is collected multiple times per second from one or more sensors, then the quantity of object detection data is improved, but measurement precision deteriorates due to occlusions and sensor measurement inaccuracies
Solution Approach 1:
The system performs preliminary actions by collecting sensor data multiple times per second before making driving decisions, creating a temporal buffer of measurements that allows for later precision correction through clustering and motion characteristic adjustment
Solution Approach 2:
The system uses feedback by comparing current sensor measurements with previous measurements, adjusting motion characteristics based on the difference between expected and actual cluster positions, thereby improving measurement precision through iterative correction
2Measurement precision
If clusters from consecutive sensor spins are associated to track objects, then object tracking accuracy is improved, but device complexity increases due to the need for surface matching algorithms and motion characteristic adjustments
Solution Approach 1:
The system segments the complex tracking problem into distinct components: cluster association between consecutive spins, motion characteristic estimation, and surface matching algorithm application, allowing each component to be optimized independently
Solution Approach 2:
The system introduces motion characteristics as an intermediary variable that mediates between raw sensor measurements and final object tracking results, simplifying the overall tracking process by providing a intermediate representation that captures essential motion information
3Reliability
If motion characteristics are adjusted based on surface matching algorithms, then reliability of driving decisions is improved, but loss of time increases due to additional processing steps
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing motion characteristics from previous sensor spins, so that when new data arrives, the system can quickly adjust using pre-prepared reference information rather than computing everything from scratch
Solution Approach 2:
The system applies partial action by adjusting only the necessary motion characteristics that have changed between consecutive spins rather than re-processing all motion parameters, reducing computational overhead while maintaining reliability
Data Source
AI summary
The technology relates to tracking objects in an environment around an autonomous vehicle. A computing system of the autonomous vehicle determines accurate motion characteristics of objects detected in its environment despite various sensor measurement limitations. By correcting motion distortion for fast moving objects and accounting for discrepancies in sensor data gathering, motion characteristics may be determined for the detected objects with enhanced accuracy. Multiple sets of correspondences are determined for clusters from multiple sensor spins, enabling better alignment using a surface matching algorithm even when clusters have fewer data points. Efficiency is also enhanced by selecting hypotheses based on confidence levels. These techniques provide for identifying the types of objects for which a yaw rate can be accurately determined. Object classification can also be improved by accumulating associated clusters corresponding to a detected object. In addition, under-or over-segmentation can be mitigated with such techniques.