Multi-Vehicle Acoustic Triangulation for Honk Source Localization
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Solution Overview
Problem
Self-driving vehicles face challenges in determining the source of horn honks or other acoustical information in complex environments, such as congested areas or when line-of-sight is occluded, which limits their ability to take corrective actions.
Innovation Solution
A method involving multiple self-driving vehicles sharing real-time acoustical sensor data to triangulate the location of the sound source, using their positions and sensor arrangements, along with additional sensor data and map information, to accurately identify the origin of the sound.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single autonomous vehicle uses its own acoustical sensors to detect sound sources, then the vehicle can detect sounds in its environment, but it cannot accurately determine which specific vehicle is honking in congested areas or when line-of-sight is occluded
Solution Approach 1:
The patent combines acoustical sensor data from multiple autonomous vehicles to collectively identify the source of a honk. By merging observations from several vehicles, the system overcomes individual vehicle limitations such as occluded line-of-sight and congested environments, enabling accurate identification of the honking vehicle through data fusion.
Solution Approach 2:
The system introduces a communication network as an intermediary to facilitate data exchange between autonomous vehicles. This mediator enables vehicles to share acoustical sensor data and coordinate their observations, allowing the group to collectively localize sound sources even when individual vehicles cannot directly observe the honking vehicle.
2Measurement precision
If multiple autonomous vehicles share and process acoustical sensor data collectively, then the localization accuracy of sound sources improves, but the system complexity increases
Solution Approach 1:
The patent segments the sound source localization task across multiple autonomous vehicles, with each vehicle independently processing its own acoustical sensor data to determine direction-of-arrival and timestamp information. This segmentation distributes the computational complexity across the fleet while maintaining high localization accuracy through coordinated data fusion.
Solution Approach 2:
The system transitions from single-vehicle two-dimensional sound localization to multi-vehicle three-dimensional spatial localization by incorporating vehicle positions, orientations, and relative geometries. This dimensional expansion enables precise triangulation of sound sources while distributing processing complexity across the network.
3Productivity
If autonomous vehicles operate independently without sharing sensor data, then each vehicle maintains operational simplicity, but the ability to respond to environmental cues like horn honks is limited
Solution Approach 1:
The system implements feedback loops where autonomous vehicles share acoustical sensor data and localization results with the fleet. This feedback mechanism enables continuous refinement of sound source identification and allows vehicles to adjust their driving operations based on collectively gathered environmental information, improving overall system responsiveness.
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 precise localization of the sound source, allowing self-driving vehicles to adjust their operations effectively, even in challenging environments, by refining the location through shared data and processing.
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
Implementation Method 2
The location and pose (e.g., position and orientation along the roadway or pitch, yaw and roll of the vehicle chassis) 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
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.