Collision Avoidance Control for Personal Mobility Using Time-to-Collision

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Solution Overview

Problem

Personal mobility devices such as electric kickboards and bicycles face a high risk of collision accidents due to their operation on roads and sidewalks, necessitating effective collision avoidance methods to ensure user safety.

Innovation Solution

A collision avoidance system that utilizes a neural network model to recognize objects in a driving image, calculate relative distances, and determine required collision times, providing collision warning guidance or controlling the device to brake when necessary, incorporating features like object tracking and Kalman filter prediction for accurate distance calculation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If personal mobility devices operate on roads and sidewalks, then transportation versatility is improved, but collision risk increases

Engineering Contradiction:

Improvetransportation versatilityVSAvoidcollision risk

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary object recognition and collision risk assessment before a collision actually occurs. The neural network model continuously analyzes the driving environment and identifies potential obstacles in advance, allowing the system to prepare collision avoidance actions proactively rather than reactively.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback by monitoring the driving environment, calculating collision risks, and adjusting control signals in real-time. The collision risk calculation unit continuously updates risk assessments based on new sensor data and object positions, creating a closed-loop control system that adapts to changing conditions.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If neural network model is used for object recognition, then recognition accuracy is improved, but processing time increases

Engineering Contradiction:

Improveobject recognition accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system applies partial action by focusing the neural network's processing on the most critical aspects of object recognition for collision avoidance. Rather than performing exhaustive analysis of all image features, the system prioritizes identifying objects and characteristics most relevant to collision risk, achieving sufficient accuracy with reduced processing time.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20240386725A1Moving body collision avoidance device, collision avoidance method and electronic device

Publication Date: 2024.11.21 THINKWARE

AI summary

There is provided a collision avoidance method of a moving body collision avoidance device including acquiring a driving image of the moving body, recognizing an object in the acquired driving image using a neural network model, calculating a relative distance between the moving body and the object based on the recognized object, calculating a required collision time between the object and the moving body based on the calculated relative distance, and controlling an operation of the moving body based on the calculated required collision time.