Acoustic Beamforming and RGB Fusion for Adverse-Weather Object Tracking
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Solution Overview
Problem
Autonomous vehicles face challenges in object detection and tracking, particularly in adverse weather conditions and low-light scenarios, where electromagnetic radiation-based sensors like cameras and LiDAR struggle with objects of low reflectance or occlusion, limiting their effectiveness.
Innovation Solution
The integration of long-range acoustic beamforming with conventional RGB visual data using a network of acoustic and visual sensors, which generates spatial beamforming maps and feature maps to enhance object detection and tracking, leveraging the unique properties of acoustic waves that propagate independently of light conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electromagnetic radiation-based sensors (camera, LiDAR, radar) are used for object detection, then object detection can be performed in normal conditions, but detection performance degrades in adverse weather conditions and low-light scenarios
Solution Approach 1:
The patent combines acoustic sensors with electromagnetic radiation-based sensors (camera, LiDAR, radar) to create a multi-modal sensing system. The acoustic beamforming component detects objects using sound waves, which propagate differently than electromagnetic waves and are less affected by adverse weather conditions. This merging of different sensing modalities allows the system to maintain reliable object detection across diverse environmental conditions where single modality systems fail.
2Reliability
If acoustic sensors are added to the sensor network, then object detection capability in adverse conditions is improved, but device complexity increases
Solution Approach 1:
The patent replaces the need for complex mechanical or optical systems with acoustic beamforming technology. Instead of relying on complex electromagnetic sensor arrays that struggle in adverse conditions, the system uses acoustic sensors that naturally penetrate fog, rain, and darkness more effectively. The acoustic beamforming algorithm processes signals from multiple acoustic sensors to achieve precise spatial localization without requiring complex hardware modifications.
3Measurement precision
If long-range acoustic beamforming is implemented, then object tracking in non-line-of-sight scenarios is improved, but computational requirements increase
Solution Approach 1:
The patent performs preliminary spatial beamforming map generation that pre-processes acoustic signals to create probability maps of object locations. This preliminary action organizes the computational work in advance, allowing the system to efficiently track objects across multiple frames without requiring intensive real-time computation for each new sensor reading. The pre-computed spatial maps enable faster query and tracking operations.
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 improves object detection and tracking performance in challenging environments by providing complementary sensory information, enabling more accurate and reliable object identification and localization, even in non-line-of-sight and partially occluded scenarios.
Implementation Method 1
generate spatial beamforming maps locating a sound source based upon acoustic signals received at the plurality of acoustic sensors
Implementation Method 2
generate visualization maps generated based upon visual signals received by the plurality of visual sensors
Data Source
AI summary
An autonomous vehicle including a network of sensors including a plurality of acoustic sensors and a plurality of visual sensors, at least one processor, and at least one memory storing instructions is disclosed. The instructions, when executed by the at least one processor, cause the at least one processor to: (i) generate spatial beamforming maps locating a sound source based upon acoustic signals received at the plurality of acoustic sensors; (ii) identify a type of an object generating the acoustic signals received at the plurality of acoustic sensors based upon comparison of the acoustic signals with a plurality of acoustic signals and respective objects stored in a dataset; and (iii) generate feature maps for an application in an autonomous vehicle driving by enhancing visualization maps generated based upon visual signals received by the plurality of visual sensors.