Motorcycle Rider Assistance System Using Multi-Modal Warning
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Solution Overview
Problem
The motorcycle industry lacks effective Advanced Driver Assistance Systems (ADAS) due to cost constraints and challenges in providing alerts in noisy, helmeted environments with limited visibility, and motorcycles behave differently than cars, requiring specialized safety measures.
Innovation Solution
A riding assistance system for motorcycles featuring a processing resource, memory, and forward-looking and backward-looking cameras to capture images, analyze time-to-collision, and generate warnings through lighting, sound, or vibration notifications, with adaptive features for lean angles and blind spots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ADAS systems are added to motorcycles, then rider safety is improved, but vehicle cost increases
Solution Approach 1:
The system segments safety functions into modular components: forward-looking camera for collision detection, backward-looking camera for blind spot monitoring, processing resource for analysis, and multiple notification channels (lights, sound, vibration). This modular approach allows selective implementation and reduces overall system cost.
Solution Approach 2:
The notification system uses multi-functional output devices that can serve multiple purposes: lights provide visual warnings for various threats, sound notifications alert riders to different hazards, and vibration provides tactile feedback. These universal components replace multiple specialized devices, reducing cost.
2Loss of information
If visual indicators are placed on the motorcycle, then collision warnings are provided, but positioning at a visible location is challenging
Solution Approach 1:
The system transitions from two-dimensional display surfaces to three-dimensional spatial notification. Lights are positioned on the motorcycle body at strategically located points, sound speakers are distributed throughout the vehicle, and vibration elements are placed on the rider interface. This spatial distribution ensures warnings are visible and perceivable from multiple angles and positions.
Solution Approach 2:
The system introduces an intermediary notification layer between the detection system and the rider. Instead of requiring direct visual contact with display screens, the system uses lights, sound, and vibration as intermediaries that can convey warning information to the rider regardless of helmet position, head orientation, or display location on the motorcycle.
3Loss of information
If alerts are provided in noisy environments, then collision warnings are delivered, but wind and engine noise interfere with notification
Solution Approach 1:
The system introduces multiple intermediary notification channels (lights, sound, vibration) that translate digital warnings into physical sensations perceivable by the rider. These intermediaries bypass the limitations of auditory-only notifications in noisy environments by providing visual and tactile alternatives that are not affected by wind or engine noise.
Solution Approach 2:
The notification system applies different notification modalities to different locations and threat types. Visual lights are positioned for maximum visibility, sound speakers are placed for optimal audio coverage, and vibration elements are located on the rider interface. This localized optimization ensures each notification channel overcomes specific environmental interference at its location.
4Reliability
If the rider wears a helmet, then head protection is provided, but viewing angle and alert reception are limited
Solution Approach 1:
The system uses intermediaries (lights, sound, vibration) that convey information to the rider without requiring direct line-of-sight viewing. The rider can perceive warnings through these intermediaries even when wearing a helmet that blocks visual displays mounted on the motorcycle, thus maintaining head protection while ensuring warning delivery.
Solution Approach 2:
The system moves from two-dimensional visual displays requiring direct viewing to three-dimensional spatial notification. Lights are positioned on the motorcycle body, sound speakers are distributed throughout, and vibration elements are placed on the rider interface. This spatial distribution ensures warnings are perceivable from multiple angles and positions, accommodating helmet wear.
5Speed
If motorcycles lean and accelerate quickly, then maneuverability is improved, but collision detection and positioning become more difficult
Solution Approach 1:
The system performs preliminary actions by continuously capturing images and pre-processing data before collision detection is needed. The forward-looking camera continuously captures images, the backward-looking camera monitors blind spots, and the processing resource pre-analyzes this data stream. This preliminary data collection and processing ensures accurate collision detection even during rapid maneuvers when there is no time for reactive measurement.
Solution Approach 2:
The system maintains continuous operation of image capture and analysis during motorcycle maneuvers. The cameras continuously capture images, and the processing resource continuously analyzes the data stream without interruption during leaning, accelerating, or braking. This continuous operation ensures collision detection accuracy is maintained despite rapid changes in motorcycle orientation and speed.
Data Source
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AI summary
A riding assistance system for a motorcycle comprising: a processing resource; a memory configured to store data usable by the processing resource; and at least one wide- angle forward-looking camera configured to be installed on the motorcycle in a manner enabling it to capture images of a scene including at least a right side and a left side in front of the motorcycle; wherein the processing resource is configured to: obtain a series of at least two images consecutively acquired by the camera; analyze a region of interest within at least a pair of consecutive images of the series to identify features having respective feature locations within the at least pair of consecutive images; determine vectors of movement of the features; and generate a warning notification upon a criterion associated with the vectors of movement being met.