Aptiv Technologies Limited

Aptiv Technologies Ag

Yang Zheng

Izzat H Izzat

Sanling Song

This document describes methods and systems directed at imaging sensor and radar fusion for multiple-object tracking. Using tracking-by-detection, an object is first detected in a frame captured by an imaging sensor, and then the object is tracked over several consecutive frames by both the imaging sensor and a radar system. The object is tracked by assigning a probability that the object identified in one frame is a same object identified in the consecutive frame. A probability is calculated for each data set captured by a sensor by a supervised-learning neural-network model using the data collected from the sensors. Then, the probabilities associated with each sensor are fused into a refined probability. By fusing the data gathered by the imaging sensor and the radar system in the consecutive frames, a safety system can track multiple objects more accurately and reliably than using the sensor data separately to track objects.

Imaging and radar fusion for multiple-object tracking

This document describes 'Multi-domain Neighborhood Embedding and Weighting' (MNEW) for use in processing point cloud data, including sparsely populated data obtained from a lidar, a camera, a radar, or combination thereof. MNEW is a process based on a dilation architecture that captures pointwise and global features of the point cloud data involving multi-scale local semantics adopted from a hierarchical encoder-decoder structure. Neighborhood information is embedded in both static geometric and dynamic feature domains. A geometric distance, feature similarity, and local sparsity can be computed and transformed into adaptive weighting factors that are reapplied to the point cloud data. This enables an automotive system to obtain outstanding performance with sparse and dense point cloud data. Processing point cloud data via the MNEW techniques promotes greater adoption of sensor-based autonomous driving and perception-based systems.

Multi-domain neighborhood embedding and weighting of sampled data

Multi-domain neighborhood embedding and weighting of point cloud data

An object detection system includes color and infrared cameras, a controller-circuit, and instructions. The color and infrared cameras are configured to output respective color image and infrared image signals. The controller-circuit is in communication with the cameras, and includes a processor and a storage medium. The processor is configured to receive and transform the color image and infrared image signals into classification and location data associated with a detected object. The instructions are stored in the at least one storage medium and executed by the at least one processor, and are configured to utilize the color image and infrared image signals to form respective first and second maps. The first map has a first plurality of layers, and the second map has a second plurality of layers. Selected layers from each are paired and fused to form a feature pyramid that facilitates formulation of the classification and location data.

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Winnetka, CA, United States of America

Yang Zheng