Acoustic Beamforming Maps for Autonomous Vehicle Object Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in object detection, particularly in adverse weather conditions and low-light scenarios, where electromagnetic radiation-based sensors like cameras and LiDAR struggle to detect objects with low reflectance or those not in their direct line-of-sight, due to limitations in electromagnetic wave propagation.
Innovation Solution
The implementation of a long-range acoustic beamforming system using a microphone array and synthetic aperture expansion, which generates spatial beamforming maps to locate sound sources and combines these with visual signals for enhanced object detection and tracking, even in challenging environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If electromagnetic radiation-based sensors (camera, LiDAR, radar) are used for object detection, then detection speed and range are improved, but detection accuracy deteriorates in adverse weather conditions and low-light scenarios
Solution Approach 1:
The patent combines electromagnetic radiation-based sensors (camera, LiDAR, radar) with acoustic sensors (microphone array) to create a multi-modal sensing system. The acoustic beamforming provides complementary information that compensates for the limitations of electromagnetic sensors in adverse weather and low-light conditions, thereby maintaining detection accuracy while preserving detection speed.
Solution Approach 2:
Acoustic sensors serve as an intermediary modality that bridges the gap in detection accuracy when electromagnetic sensors fail. Sound waves propagate differently than electromagnetic waves and are less affected by fog, rain, and darkness, providing a reliable complementary channel for object detection in challenging environmental conditions.
2Measurement precision
If acoustic beamforming is used to improve object detection in adverse weather, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The acoustic sensing system is segmented into multiple independent microphone elements arranged in an array. Each microphone captures acoustic signals independently, and the beamforming algorithm processes these segmented signals to achieve spatial filtering and direction-of-arrival estimation. This segmentation enables improved detection accuracy while keeping individual sensor elements simple and manageable.
Solution Approach 2:
The patent replaces complex mechanical scanning systems with signal processing-based acoustic beamforming. Instead of physically moving sensors to scan the environment, the system uses digital signal processing techniques to electronically steer beams and extract spatial information, reducing mechanical complexity while maintaining or improving detection capabilities.
3Measurement precision
If microphone array size is increased to improve spatial resolution, then object localization precision is improved, but device size and cost increase
Solution Approach 1:
The patent transitions from analyzing acoustic signals in the spatial domain to analyzing them in the frequency domain using Fourier transforms and beamforming algorithms. This dimensional transformation enables the extraction of spatial information (direction-of-arrival, range) without requiring a proportionally larger physical microphone array, thereby achieving high localization precision with a compact sensor configuration.
Solution Approach 2:
The system achieves improved localization precision by changing the processing parameters rather than simply increasing the physical size of the microphone array. Advanced beamforming algorithms, frequency domain processing, and synthetic aperture techniques allow the system to extract maximum spatial information from a given array configuration, effectively increasing resolution without proportionally increasing array area.
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 adverse weather and low-light conditions by providing complementary acoustic data that enhances the resolution and accuracy of object localization, enabling better autonomous vehicle navigation and behavior control.
Implementation Method 1
acoustic signals received at the plurality of microphones of the microphone array
Implementation Method 2
generate spatial beamforming maps locating a sound source using a beamforming model corresponding to acoustic signals
Implementation Method 3
apply a synthetic aperture expansion to the acoustic signals to increase resolution of the spatial beamforming maps
Data Source
AI summary
An autonomous vehicle including a microphone array of a plurality of microphones, a visual sensor network configured to receive visual signals, 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 using a beamforming model corresponding to acoustic signals received at the plurality of microphones of the microphone array; (ii) apply a synthetic aperture expansion to the acoustic signals to increase resolution of the spatial beamforming maps; and (iii) generate feature maps for an application in autonomous vehicle driving by combining the improved spatial beamforming maps with visualization maps generated based on the visual signals received by the visual sensor network.