Occluded Emergency Vehicle Detection via Audio Reflection Analysis
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Solution Overview
Problem
Autonomous vehicles face challenges in detecting and responding to emergency vehicles, especially in complex environments where direct sounds are indistinguishable from reflected sounds, and emergency vehicles may be occluded or lack visual indicators.
Innovation Solution
The use of audio sensors to capture and analyze sound data, combined with map and perception data, to determine the direction of arrival and characteristics of sounds, allowing the vehicle to differentiate between direct and reflected sounds and locate occluded emergency vehicles through angular spectrums and amplitudes, even in the absence of visual cues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If audio sensors are used to detect emergency vehicles, then the vehicle can detect emergency vehicles in complex environments, but it becomes difficult to distinguish between direct sounds and reflected sounds
Solution Approach 1:
The patent introduces a temporal dimension by analyzing sound characteristics over time. It compares direct sound arrivals with reflected sound arrivals at different time points, using the time difference as an additional dimension to distinguish between direct and reflected sounds. This temporal analysis allows the system to differentiate sound sources even in complex acoustic environments.
Solution Approach 2:
The patent uses map data and perception data as intermediary information to assist in sound source differentiation. By combining audio data with spatial map information and visual perception data, the system creates a multi-modal representation that helps disambiguate between direct and reflected sounds, particularly when visual indicators are available to confirm emergency vehicle locations.
2Measurement precision
If visual indicators are used to detect emergency vehicles, then the vehicle can identify emergency vehicles, but it fails when emergency vehicles are occluded or in dark environments
Solution Approach 1:
The patent creates a multi-functional detection system that can operate in multiple conditions. The audio-based detection mechanism serves as a universal detector that works both when visual indicators are visible and when they are not (occluded or dark environments). By making the audio detection system multi-functional, it can adapt to various environmental conditions where traditional visual-only systems would fail.
Solution Approach 2:
The patent substitutes optical detection (visual indicators) with acoustic detection (audio sensors) for emergency vehicle identification. This replacement allows the system to detect emergency vehicles through sound waves rather than relying on light-based visual indicators, enabling operation in dark environments or when vehicles are occluded from visual view.
3Reliability
If the vehicle yields to emergency vehicles, then safe navigation is ensured, but the vehicle must respond promptly to avoid delays
Solution Approach 1:
The patent performs preliminary detection and classification of emergency vehicles using audio sensors before the vehicle needs to make navigation decisions. By continuously monitoring acoustic environments and pre-identifying emergency vehicles, the system prepares response information in advance, reducing the time needed to react when an emergency vehicle is detected and ensuring prompt yielding while maintaining safe navigation.
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 enhances the vehicle's ability to accurately and promptly respond to emergency vehicles by improving sound differentiation and localization, even when emergency vehicles are occluded or in dark environments, ensuring safe navigation and priority handling.
Implementation Method 1
The vehicle may include one or more audio sensors to capture audio data representing sound in an environment where the vehicle is located
Data Source
AI summary
Techniques for determining information associated with sounds detected in an environment based on audio data and map data or perception data are discussed herein. A vehicle can use map data and/or perception data to distinguish between multiple audio signals or sounds. A direct source of sound can be distinguished from a reflected source of sound by determining a direction of arrival of sounds and which objects the directions of arrival are associated with in the environment. A reflected sound can be received without receiving a direct sound. Based on the reflected sound and map data or perception data, characteristics of sound in an occluded region of the environment may be determined and used to control the vehicle.