AI Bicycle Hazard Detection and Early Warning in Traffic
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Solution Overview
Problem
Road biking in the presence of cars poses significant dangers due to the high speed and weight of cars, leading to a high risk of injury or death, with existing safety solutions like lights, reflectors, and helmets having limitations in preventing accidents and injuries.
Innovation Solution
An artificially intelligent mobility safety system for bicycles that incorporates sensors, AI algorithms, and alert mechanisms to provide early warning signals and alerts to users, using a combination of hardware and software components to identify potential hazards and adapt to changing traffic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional safety equipment like lights, reflectors, and helmets are used, then visibility and injury severity are improved, but proactive hazard avoidance capability is not achieved
Solution Approach 1:
The patent replaces passive mechanical safety equipment (lights, reflectors, helmets) with an intelligent system using sensors, processors, and communication devices that actively detect hazards and communicate risks to cyclists, enabling proactive rather than reactive safety
Solution Approach 2:
The system enables cyclists to independently assess traffic conditions and receive personalized risk assessments without requiring external infrastructure or intervention from other road users, making the safety system self-contained and autonomous
2Object-affected harmful factors
If passive safety equipment is used, then injury severity is reduced, but accident prevention capability is limited
Solution Approach 1:
The system performs preliminary assessment of traffic conditions and identifies potential hazards before cyclists encounter them, providing advance warning that enables preventive action rather than merely mitigating consequences after accidents occur
3Loss of information
If basic visibility equipment is used, then detection of traffic conditions is improved, but real-time hazard identification is not achieved
Solution Approach 1:
The system uses multi-functional sensors that can detect various traffic conditions (vehicle presence, speed, distance, road conditions) and processes this information to provide comprehensive hazard identification, going beyond simple visibility enhancement
Data Source
AI summary
In some examples, a mobility safety system includes a real-time data capturing component to capture data relating to conditions within a traffic environment using at least one of a camera, a radar sensor, a LiDAR sensor, a proximity sensor, an inertial measurement unit (IMU), and a global positioning system (GPS), at least one trained model to generate a risk estimation related to a mobility platform within the traffic environment, the at least one trained model being trained using at least one of supervised, unsupervised and semi-supervised learning, a risk estimation component to generate the risk estimation, and an alert activation component to generate a first alert directed at an operator of the mobility platform based on the risk estimation.