Multi-Vehicle Acoustic Localization for Horn Source Detection
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Solution Overview
Problem
Self-driving vehicles face challenges in determining the source of horn honks or other sounds in complex environments, such as congested areas or noisy conditions, which limits their ability to take corrective actions.
Innovation Solution
The technology employs a method where multiple self-driving vehicles share real-time acoustical information, using triangulation and other localization techniques based on the position and orientation of each vehicle, along with sensor data from other sensors and map information, to accurately identify the source of sounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple vehicles share and process acoustical information, then sound source localization precision is improved, but system complexity and communication requirements increase
Solution Approach 1:
The system divides the sound source localization task across multiple autonomous vehicles, where each vehicle independently processes acoustical data from its own sensors and shares results with the fleet. This segmentation allows the complex localization problem to be distributed, improving overall precision while managing individual vehicle complexity.
Solution Approach 2:
The patent combines acoustical information from multiple vehicles' sensors to create a unified sound source localization solution. By merging data from distributed microphones across the fleet and processing it collectively, the system achieves higher localization precision than any single vehicle could attain alone.
2Loss of time
If acoustical information is shared across the fleet in real-time, then response time to emergency situations is improved, but communication bandwidth requirements increase
Solution Approach 1:
The system extracts only the essential acoustical information needed for sound source localization from each vehicle's sensor data, rather than transmitting complete raw audio streams. This extraction approach reduces communication bandwidth requirements while maintaining the ability to achieve rapid response times for emergency situations.
Solution Approach 2:
Acoustical data is pre-processed on each vehicle before transmission to the fleet, with initial filtering and feature extraction performed locally. This preliminary action reduces the volume of data requiring fleet-wide communication, thereby decreasing bandwidth requirements while preserving critical information for timely emergency response.
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 enables self-driving vehicles to accurately localize the source of sounds, allowing them to take appropriate actions, such as adjusting their driving behavior or responding to emergency situations, even in challenging environments.
Implementation Method 1
obtaining, by one or more acoustical sensors of a perception system of the vehicle, audio sensor data, the one or more acoustical sensors being configured to detect sounds in an external environment around the vehicle
Data Source
AI summary
The technology relates to determining a source of a horn honk or other noise in an environment around one or more self-driving vehicles. Aspects of the technology leverage real-time information from a group of self-driving vehicles regarding received acoustical information. The location and pose of each self-driving vehicle in the group, along with the precise arrangement of acoustical sensors on each vehicle, can be used to triangulate or otherwise identify the actual location in the environment for the origin of the horn honk or other sound. Other sensor information, map data, and additional data can be used narrow down or refine the location of a likely noise source. Once the location and source of the noise is known, each self-driving vehicle can use that information to modify current driving operations and/or use it as part of a reinforcement learning approach for future driving situations.