Cyclist Safety Bubble Communication for Autonomous Vehicle Navigation
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Solution Overview
Problem
Existing autonomous and semi-autonomous motor vehicle systems often fail to adequately ensure the safety of bicyclists on the road, as they respond to cyclist presence without incorporating cyclist communication or safety preferences.
Innovation Solution
A system comprising a mobile computing device for bicyclists that communicates their location and preferred safety distance to a server, which calculates an exclusion zone and sends these coordinates to a motor vehicle's navigation system, preventing the vehicle from entering the designated safe area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicle systems use visual camera data or LIDAR to detect cyclists, then the system can identify cyclist presence, but the system cannot incorporate cyclist safety preferences or communication
Solution Approach 1:
A mobile computing device worn by the cyclist acts as an intermediary, capturing cyclist presence data and transmitting it to the autonomous vehicle system. This intermediary enables two-way communication, allowing the vehicle to receive not only detection data but also cyclist safety preferences and contextual information that would otherwise be lost.
Solution Approach 2:
The system implements feedback by having the mobile computing device continuously transmit cyclist location and safety preference data to the autonomous vehicle. This creates a closed-loop communication system where the cyclist's preferences directly influence vehicle behavior, transforming one-way detection into interactive safety management.
2Extent of automation
If the autonomous vehicle responds to cyclist presence based on its own programming, then the vehicle can maintain operational autonomy, but the system does not respect the cyclist's safety needs
Solution Approach 1:
The system dynamically adjusts the level of automation based on cyclist input. When cyclist safety preferences are received, the vehicle transitions from purely autonomous decision-making to a collaborative mode where cyclist preferences modify vehicle behavior. This dynamic adaptation maintains operational autonomy while incorporating human safety needs.
Solution Approach 2:
The system changes operational parameters by adjusting the vehicle's safety distance and passing behavior based on cyclist preferences transmitted through the mobile computing device. This allows the autonomous system to adapt its parameters in real-time to respect individual cyclist safety needs while maintaining automated operation.
3Reliability
If the mobile computing device calculates and transmits exclusion zone coordinates to the motor vehicle, then cyclist safety is enhanced, but additional communication infrastructure is required
Solution Approach 1:
The mobile computing device performs multiple functions: it detects cyclist presence, calculates safety parameters, determines exclusion zones, and communicates with the vehicle. By consolidating these functions into a single universal device, the system enhances safety without proportionally increasing overall system complexity.
Solution Approach 2:
The mobile computing device autonomously calculates exclusion zone coordinates based on cyclist location and safety parameters, then automatically transmits this data to the vehicle without requiring manual intervention. This self-service capability simplifies the user interface while maintaining comprehensive safety functionality.
Data Source
AI summary
Safety for bicyclists while travelling on a road near to certain motor vehicles is achieved through the use of systems and methods that communicate the physical location of the bicyclist in relation to the motor vehicle. The systems and methods allow a bicyclist to generate a “safety bubble” of an exclusion zone set of location coordinates. The motor vehicle is automatically prompted to navigate away from the exclusion zone. The exclusion zone of location coordinates may be generated based on the bicyclist user's preferences, as well as legal data and road data. By drawing from these several parameters, the exclusion zone may be sized and located so as to best meet the bicyclist's need without unduly impacting traffic flow.