Pedestrian Crossing Warning System Using Beamformed Audio
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Solution Overview
Problem
Current pedestrian crossing warning systems provide generic warnings that can distract non-risk individuals and fail to effectively assess and mitigate the collision risk for pedestrians with sensory deficiencies or distractions, such as those using mobile devices.
Innovation Solution
A pedestrian crossing warning system utilizing multi-modal technology, including sensors and machine learning models, to assess the risk level of pedestrians based on their trajectory, attributes, and environmental factors, and emit targeted warnings, such as beamformed audio signals, to reduce the risk of collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic warnings are provided to all pedestrians in the vicinity, then the warning system is simple to implement, but non-risk individuals are distracted and the warning effectiveness is reduced
Solution Approach 1:
The warning system segments pedestrians into different risk groups based on trajectory analysis, attributes (age, mobility aids, hearing aids), and environmental factors. Instead of treating all pedestrians uniformly, the system applies targeted warnings only to those calculated to be at risk, thereby maintaining simplicity while improving effectiveness.
Solution Approach 2:
The system applies different warning strategies to different spatial locations and pedestrian types. Beamformed audio signals are directed specifically at at-risk pedestrians based on their position and characteristics, providing localized warnings rather than general area warnings, which reduces distraction to non-risk individuals.
2Reliability
If beamformed audio signals are used to target specific pedestrians, then warning effectiveness is improved, but device complexity increases
Solution Approach 1:
The system replaces complex mechanical or manual assessment methods with automated sensor-based detection and machine learning models. Sensors capture trajectory, attributes, and environmental data, while ML models automatically calculate risk levels and determine appropriate warnings, reducing the need for complex manual intervention while maintaining high effectiveness.
3Measurement precision
If multiple sensors and machine learning models are deployed to assess pedestrian risk accurately, then collision risk assessment is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary assessments by continuously monitoring pedestrian trajectories and attributes before collision risk becomes critical. Machine learning models pre-calculate risk levels based on detected attributes (hearing aids, mobility aids, age groups) and trajectory patterns, enabling proactive warnings before the situation escalates, thus reducing critical processing time.
Solution Approach 2:
The system changes assessment parameters dynamically based on detected pedestrian attributes. Instead of using fixed assessment criteria, the ML models adjust risk calculation parameters according to detected characteristics (e.g., hearing aids indicate reduced auditory warning effectiveness, mobility aids indicate slower response time), improving accuracy while optimizing processing efficiency through attribute-based parameter adaptation.
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
The system effectively reduces the risk of collisions by providing targeted warnings that are perceivable only to pedestrians at risk, minimizing distractions to others and accounting for individual sensory limitations and environmental conditions.
Implementation Method 1
the vehicle emits a beamformed audio signal directed at the current position of the elderly person
Data Source
AI summary
Systems and methods are disclosed herein for a pedestrian crossing warning system that may use multi-modal technology to determine attributes of a person and provide a warning to the person in response to a calculated risk level to effect a reduction of the risk level. The system may utilize sensors to receive data indicative of a trajectory of a person external to the vehicle. Specific attributes of the person such as age or walking aids may be determined. Based on the trajectory data and the specific attributes, a risk level may be determined by the system using a machine learning model. The system may cause emission of a warning to the person in response to the risk level.