Electric Scooter Behavior Prediction for Vehicle Hazard Alerts
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Solution Overview
Problem
There is a growing need to autonomously adapt driving near schools, particularly in addressing the challenges posed by electrical scooters, which can be unpredictable and pose risks to vehicle safety due to their behavior on roads and sidewalks, especially in complex traffic conditions.
Innovation Solution
A system and method for electrical scooter alert using sensors to identify and track scooters, build behavioral models, predict their trajectories, and generate alerts to ensure safe vehicle navigation, incorporating machine learning and sensor data from various sources like video, audio, radar, and LIDAR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based detection systems are used to identify electrical scooters, then basic detection capability is achieved, but detection precision and reliability are insufficient due to the unpredictable behavior and small size of scooters
Solution Approach 1:
The patent combines multiple sensor types (cameras, LIDAR, radar) into an integrated sensing system that fuses data from different modalities. This multi-sensor fusion approach improves detection precision for small, unpredictable objects like electrical scooters by compensating for the limitations of individual sensors through data correlation and cross-validation.
Solution Approach 2:
The system implements dynamic detection thresholds and adaptive tracking parameters that adjust in real-time based on environmental conditions, scooter behavior patterns, and traffic context. This dynamic adaptation allows the system to maintain high detection precision for unpredictable scooter movements without requiring overly complex fixed-threshold systems.
2Reliability
If behavioral modeling and trajectory prediction are implemented to predict scooter behavior, then collision risk reduction is improved, but computational time and processing complexity increase
Solution Approach 1:
The system pre-computes behavioral models and trajectory predictions for multiple potential scooter actions before they actually occur. By preparing prediction frameworks in advance and maintaining pre-trained behavioral models for common scooter scenarios, the system can rapidly evaluate potential risks without performing complex computations in real-time, thus reducing computational time while maintaining high reliability.
Solution Approach 2:
The patent replaces complex real-time mechanical computation with pre-trained machine learning models and lookup tables that store pre-calculated behavioral patterns. This substitution allows the system to quickly retrieve and apply prediction results without performing heavy computational operations during critical decision-making moments, balancing reliability with speed.
3Loss of information
If comprehensive sensor data from video, audio, radar, and LIDAR is integrated, then situational awareness is enhanced, but device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the integration process into distinct stages: individual sensor data processing, feature extraction, data association, and fusion. Each sensor type processes its data independently through specialized modules before results are combined. This segmented approach allows comprehensive situational awareness to be achieved while managing complexity through modular, organized processing pipelines rather than monolithic integration.
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 provides timely and accurate alerts, enabling vehicles to respond to potential hazards from electrical scooters, enhancing safety by predicting their behavior and impact on vehicle progress, thus improving situational awareness and reducing collision risks.
Implementation Method 1
sensors to identify and track scooters
Implementation Method 2
sensors to identify and track scooters
Data Source
AI summary
There may be provided a method for electrical scooter alert, the method may include sensing sensed information about an environment of a vehicle; detecting, based on the sensed information, a situation related to the environment; detecting, based on the sensed information, a electrical scooter within the environment; predicting, using a machine leaning process and based in the situation, a future behavior of the electrical scooter and an impact of the future behavior of the electrical scooter on a future progress of the vehicle; and responding to the predicting.