Map-Anchored Object Detection for Sparse Long-Range Sensor Data
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting objects at long ranges using limited sensor data, particularly due to sparse LIDAR returns and uncertainty in range estimation.
Innovation Solution
The system anchors object detections to map data, processing sensor data fused with map data to determine the position of detected objects in the mapped environment. This approach constrains the detection solution space, simplifies the detection task, and allows for range-invariant object detection models that can operate effectively at varying ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data alone is used for object detection, then the detection process is independent of external data, but detection accuracy deteriorates at long ranges due to sparse sensor returns
Solution Approach 1:
The patent combines sensor data with map data to create a fused representation of the environment. Map markers from high-definition maps are integrated with sensor detections, allowing the system to leverage the density and precision of map data to compensate for sparse sensor returns at long ranges, thereby improving detection accuracy without relying solely on sensor data quantity
Solution Approach 2:
Map data serves as an intermediary that bridges the gap between limited sensor data and the need for accurate long-range detection. The map provides a dense lattice of reference points that mediate the detection process, enabling the system to infer object positions and characteristics even when direct sensor returns are sparse or absent
2Measurement precision
If map data is fused with sensor data, then detection accuracy improves at long ranges, but system complexity increases
Solution Approach 1:
The detection process is segmented into distinct stages: map data retrieval, sensor data processing, data fusion, and detection output. By dividing the complex task of fusing map and sensor data into manageable segments, the system can handle the complexity systematically, processing each data type and their integration separately rather than attempting to process them simultaneously as a monolithic operation
Solution Approach 2:
Map data is retrieved and processed in advance before the actual detection occurs. The high-definition map provides pre-computed lane markers, road boundaries, and environmental features that are ready for immediate integration with sensor data, eliminating the need for real-time computation of these reference elements and reducing overall system complexity
3Measurement precision
If traditional object detection is used without map anchoring, then the detection model is simpler, but the solution space for detection outputs is unconstrained and less accurate
Solution Approach 1:
The detection process applies local constraints by anchoring detections to specific map markers rather than treating the entire scene uniformly. Each detection is localized around relevant map features such as lane markers or road boundaries, allowing the system to focus computational resources on locally relevant areas and improve precision without requiring complex global optimization
Solution Approach 2:
The patent changes the parameter space of the detection problem by transforming map coordinates into sensor reference frames and vice versa. This parameter transformation allows the detection model to operate in a constrained solution space defined by map geometry, improving detection precision while the transformation mathematics provides a structured framework that manages the apparent complexity
4Length of stationary object
If LIDAR is used for long-range detection, then detection range is extended, but data sparsity increases significantly
Solution Approach 1:
Map data acts as an intermediary that compensates for LIDAR data sparsity at long ranges. The high-definition map provides a dense lattice of reference points and environmental features that fill in the gaps where LIDAR returns become sparse, enabling the system to maintain accurate detection and environmental understanding at extended ranges without being limited by sensor data density
Solution Approach 2:
The system merges LIDAR point cloud data with map-derived geometric primitives to create a complete environmental representation. By combining the range-extending capability of LIDAR with the density-providing map data, the system achieves both long detection range and sufficient data density for accurate object detection and scene understanding
Data Source
AI summary
An example method includes (a) obtaining sensor data descriptive of an environment of an autonomous vehicle; (b) obtaining a plurality of travel way markers from map data descriptive of the environment; (c) determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment; and (d) generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object.