AI E-Bike Compliance Control Using Mobile Sensing and Alerts
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Solution Overview
Problem
Conventional electric bikes lack smart assistance and controls, leading to user misuse and safety concerns, especially in busy city environments, as they do not provide real-time guidance or enforcement of traffic rules and safety standards.
Innovation Solution
An electric bike system utilizing Artificial Intelligence (AI) that includes cameras, sensors, and a mobile application to capture user and path information, determining deviations from predefined thresholds and generating alerts or restricting bike functions to ensure compliance with traffic rules and safety standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional electric bikes are used without smart assistance systems, then device complexity is reduced and ease of manufacture is improved, but safety and compliance monitoring capabilities deteriorate
Solution Approach 1:
The mobile device (smartphone) is utilized to perform multiple functions including capturing user information via camera, monitoring sensor data, tracking path information, processing images with AI models, generating alerts, and controlling bike functions. This multi-functional approach improves safety without requiring separate dedicated hardware for each function, thus limiting the increase in device complexity.
Solution Approach 2:
The mobile device acts as an intermediary between the user and the bike's control system. It captures information, processes it through AI models, and then communicates control signals to the bike's controller. This intermediary approach allows sophisticated safety monitoring while keeping the bike's internal control system relatively simple.
2Reliability
If AI-based monitoring systems are implemented to detect deviations from traffic rules, then compliance and safety are improved, but device complexity and processing requirements increase
Solution Approach 1:
Traffic rules and safety thresholds are pre-configured in the system before operation. The mobile application has access to pre-stored traffic rule data and predefined thresholds for safe operation. This preliminary setup allows the system to automatically monitor and detect violations without requiring complex real-time rule interpretation, reducing processing complexity during actual operation.
Solution Approach 2:
The system continuously monitors user information, sensor information, and path information, compares them against predefined thresholds, and provides immediate feedback through alerts when deviations are detected. This feedback mechanism ensures compliance by real-time monitoring and user notification, improving reliability without requiring overly complex predictive algorithms.
3Reliability
If real-time information capture and processing is performed to monitor user behavior and path, then safety monitoring capability is improved, but use of energy and processing power increase
Solution Approach 1:
The mobile application captures and processes information at periodic intervals rather than continuously. It periodically captures user information via camera, sensor information from sensors, and path information from GPS, then processes these batches of data. This periodic approach provides adequate safety monitoring while significantly reducing energy consumption compared to continuous real-time processing.
Solution Approach 2:
The system captures information (images, sensor data, location data) as copies or representations of the actual state, processes these copies, and uses them for monitoring purposes. This allows safety monitoring without requiring direct manipulation of the physical system, reducing energy and processing requirements.
4Reliability
If progressive function curtailment is implemented in response to violations, then enforcement of safety rules is improved, but user convenience deteriorates
Solution Approach 1:
The system takes preliminary anti-action by progressively curtailing bike functions in response to detected violations before more serious safety incidents can occur. When deviations from traffic rules or safe operation thresholds are detected, the system sends deactivating signals to the controller to progressively restrict bike functions, preventing further violations and protecting user safety.
Solution Approach 2:
The system provides continuous feedback to users through alerts when violations are detected, and implements progressive function curtailment as a corrective measure. This feedback loop maintains safety enforcement while giving users opportunities to correct behavior, balancing safety requirements with user convenience.
Data Source
AI summary
The disclosure relates to an electric bike controlled using Artificial Intelligence (AI). The electric bike includes a mobile mount that is configured to receive a mobile device. A controller is configured to capture user information via a front camera. Further, sensors configured in the mobile device and the electric bike capture sensor information associated with the electric bike. The electric bike includes a rear camera to capture path information associated with a path being used to ride the electric bike. A first mobile application converts each of user information, sensor information, and path information into corresponding information states and determines whether these information states are deviating from associated predefined thresholds. In response to determining a deviation an alert signal is generated and a deactivating signal is transmitted to the controller to progressively curtail one functioning of the electric bike.