Collision Avoidance Systems

Through image recognition technology and machine learning, the limitations of vehicle collision avoidance system in the prior art on obstacle detection are solved, and accurate detection and identification of various obstacles are achieved, thereby improving the safety and operation efficiency of the vehicle.

JP7653500B2Active Publication Date: 2025-03-28HAMADA HEAVY IND
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Patent Information

Application Number
JP2023201749
Authority / Receiving Office
JP · JP
Patent Type
Patents
Current Assignee / Owner
Filing Date
2023-11-29
Publication Date
2025-03-28
Estimated Expiration
2042-06-28

AI Technical Summary

Technical Problem

The prior art has limitations in detecting and avoiding vehicle collisions, and cannot effectively detect other obstacles other than the vehicle in front, and the obstacle detection function when the vehicle reversal is insufficient, resulting in improved safety and practicality.

Method used

Image recognition technology is used in combination with machine learning to detect collision avoidance targets around the vehicle, and adjust the alarm output according to the type of target and the operating status of the vehicle to ensure that collisions can be effectively detected and avoided in any vehicle attitude.

Benefits of technology

Accurate detection and identification of various collision avoidance targets is achieved, unnecessary alarms and collisions are avoided, and the safety and operation efficiency of the vehicle are improved.

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Abstract

To provide a collision-avoidance system capable of reliably and efficiently preventing contact between a vehicle and a collision avoidance object by detecting the collision avoidance object regardless of attitudes or directions of various types of collision avoidance objects existing around the vehicle being driven, specifying a type thereof, and preventing excessive detection and erroneous detection.SOLUTION: A collision-avoidance system includes: a machine learning setting part 11 executing machine learning on the basis of a learning image including a collision avoidance object to be detected and constructing a machine learning model; an image acquisition part 12 for acquiring an image obtained by photographing a periphery of a vehicle while the vehicle is driven; a machine learning inference part 14 for discriminating whether a collision avoidance object exists around the vehicle from the image acquired by the image acquisition part 12 and the machine learning model; a collision avoidance setting part 15 for acquiring position information of the collision avoidance object detected by the machine learning inference part 14 and determining a collision possibility of the vehicle and the collision avoidance object; and an alarm output part 16 for outputting an alarm to a driver of the vehicle on the basis of the determination of the collision avoidance setting part 15.SELECTED DRAWING: Figure 1
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