Intelligent Ladar Threat Detection for Low-Latency Motion Planning
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Solution Overview
Problem
Conventional autonomous vehicle motion planning systems face delays due to the hierarchical master-slave relationship between sensors and motion planning systems, where sensor data ingestion, storage, and analysis burden the motion planning system, leading to decision-making delays.
Innovation Solution
A collaborative decision-making model between sensors and the motion planning system, where intelligent sensors detect anomalies and notify the system via priority messaging, allowing for faster processing and reducing latency by enabling the sensors to issue priority messages or interrupts, and employing compressive sensing and co-bore sited camera-ladar systems for low-latency threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a hierarchical master-slave relationship is used between motion planning system and sensors, then the motion planning system can centrally control sensor data acquisition, but the motion planning system experiences decision-making delays due to heavy processing burdens from ingesting, storing, retrieving, and analyzing sensor data
Solution Approach 1:
The patent segments the centralized processing burden by distributing intelligence to individual sensors. Each sensor independently performs anomaly detection and generates alerts, separating the detection function from the motion planning system. This segmentation allows the motion planning system to receive only critical alerts rather than processing all raw sensor data, thereby reducing decision-making delay while maintaining automated control.
Solution Approach 2:
The patent introduces an intermediary alerting mechanism between sensors and the motion planning system. Intelligent sensors act as intermediaries that pre-process sensor data, identify anomalies, and communicate only significant findings to the motion planning system. This intermediary layer filters out routine data, reducing the processing burden on the motion planning system and enabling faster response times.
2Measurement precision
If the motion planning system processes all sensor data centrally, then comprehensive analysis can be performed, but the processing time increases leading to latency in motion planning updates
Solution Approach 1:
The patent applies preliminary action by having sensors perform anomaly detection and pre-processing before data reaches the motion planning system. Sensors independently analyze their data streams and pre-identify potential threats, performing the initial detection work beforehand. This preliminary action reduces the processing load on the motion planning system while maintaining detection accuracy, as only pre-validated anomalies require further central analysis.
Solution Approach 2:
The patent extracts the anomaly detection function from the central motion planning system and places it at the sensor level. By taking out this specific processing task and assigning it to individual sensors, the system achieves faster local detection while the motion planning system focuses only on high-level decision-making based on sensor alerts, thereby improving overall processing speed without sacrificing detection precision.
3Speed
If intelligent sensors issue priority messages for detected threats, then the motion planning system can quickly respond to new threats, but the system requires a collaborative architecture that moves intelligence into sensors
Solution Approach 1:
The patent applies dynamics by creating a flexible, adaptive architecture where sensors dynamically assume intelligent detection capabilities. The system transitions from a static hierarchical structure to a dynamic collaborative model where sensors can independently generate priority alerts when threats are detected. This dynamic adaptation enables faster response speeds while the increased intelligence distribution manages architecture complexity through modular sensor design.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces latency in obstacle detection and motion planning, enabling vehicles to respond promptly to dynamic obstacles, improving safety and reducing the time required for motion planning updates.
Implementation Method 1
a ladar system that employs compressive sensing to reduce the number of ladar shots required to capture a frame of sensor data
Implementation Method 2
the intelligent sensor can be a ladar system
Data Source
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AI summary
Systems and methods are disclosed for vehicle motion planning where a sensor, such as a ladar system, is used to detect threatening or anomalous conditions within the sensor's field of view so that priority warning data about such conditions can be inserted at low latency into the motion planning loop of a motion planning computer system for the vehicle. Also disclosed herein is a ladar system that includes a ladar transmitter, ladar receiver, and camera, where the camera that is co-bore sited with the ladar receiver, the camera configured to generate image data corresponding to a field of view for the ladar receiver. Also disclosed are techniques where a ladar system can estimate intra-frame motion for an object within a field of view of the ladar system using a tight cluster of ladar pulses. Further still, a ladar transmitter is disclosed that can be controlled to target range points based on any of a plurality of defined shot list frames. A processor can process data about the field of view such as range data and/or camera data to make selections as to which of the defined shot list frames should be selected for a given frame of ladar data.