Multi-Sensor Track Confidence Modeling for False Positive Suppression
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Solution Overview
Problem
Existing object detection and tracking systems in autonomous vehicles face challenges due to variations in sensor data formats and processing algorithms, leading to inconsistent detection results across different sensor types, which can result in false positives and excessive reliance on individual sensors.
Innovation Solution
A combined track confidence and classification model that aggregates data from multiple perception pipelines, including lidar, camera, and radar sensors, to generate a unified track confidence metric and classification, reducing reliance on individual sensors and improving detection accuracy by integrating data from various sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensor types are used for detection and tracking, then detection coverage and reliability are improved, but sensor data format variations and processing differences lead to inconsistent detection results
Solution Approach 1:
The patent combines detection results from multiple sensor types (lidar, radar, camera) into a unified track by merging detection data and generating a single confidence metric that reflects the consolidated reliability across all sensors, thereby improving consistency while maintaining the benefits of multi-sensor coverage
Solution Approach 2:
The system transforms diverse sensor data formats into a standardized representation by converting detection results from different sensor types into a common confidence metric scale, enabling consistent comparison and evaluation across heterogeneous sensor inputs
2Adaptability or versatility
If different detection algorithms process sensor data from various sensor types, then adaptability to different sensor formats is improved, but the detections generated by different sensor types differ
Solution Approach 1:
The patent introduces an intermediary confidence metric that mediates between different detection algorithms and sensor types, translating diverse algorithm outputs into a standardized confidence representation that enables consistent evaluation while preserving the adaptability benefits of multiple processing approaches
3Device complexity
If individual sensors are relied upon for detection, then processing simplicity is maintained, but false positives increase and detection accuracy decreases
Solution Approach 1:
The system merges detection results from multiple sensors to generate a unified track confidence metric, where the combined evidence from independent sensors reduces false positives and improves detection accuracy while maintaining relatively simple processing through confidence aggregation
Data Source
AI summary
Techniques are disclosed for a combined machine learned (ML) model that may generate a track confidence metric associated with a track and/or a classification of an object. Techniques may include obtaining a track. The track, which may include object detections from one or more sensor data types and/or pipelines, may be input into a machine-learning (ML) model. The model may output a track confidence metric and a classification. In some examples, if the track confidence metric does not satisfy a threshold, the ML model may cause the suppression of the output of the track to a planning component of an autonomous vehicle.