Vehicle Awareness Notifications Using Position-Sample Vicinity Detection
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Solution Overview
Problem
Current solutions for warning motor vehicle drivers about vulnerable vehicles like bicycles are inefficient, resource-intensive, unreliable, and produce many false positives, often requiring a clear line of sight and leading to alert fatigue.
Innovation Solution
A system and method using client computing devices associated with vehicles to analyze historical position samples and determine geographical vicinity, producing awareness notifications based on predefined thresholds and velocity data to alert drivers of potential collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera-based ADAS solutions are used to detect vulnerable vehicles, then collision warning capability is improved, but the system requires clear line of sight and produces false positives
Solution Approach 1:
The patent introduces server computing devices as intermediaries that aggregate and analyze location data from multiple client devices. Instead of relying on direct camera detection between vehicles, the server acts as a mediator that processes location information and generates awareness notifications, eliminating the need for clear line of sight while reducing false positives through centralized analysis
Solution Approach 2:
The patent replaces the mechanical/optical camera-based detection system with an electronic location data processing system. By substituting physical camera detection with digital location tracking and server-based analysis, the system eliminates line of sight requirements and reduces false positives through computational processing of location information
2Reliability
If centralized server processing is used for location data analysis, then comprehensive vehicle awareness is improved, but computational and communicational load on servers increases
Solution Approach 1:
The patent segments the computational workload by distributing location data collection to client devices while keeping only essential analysis functions on the server. Each client device independently tracks and reports its own location, reducing the server's processing burden while maintaining comprehensive vehicle awareness through centralized data aggregation
Solution Approach 2:
The patent implements partial processing at the client level and partial processing at the server level. Client devices perform basic location tracking and filtering before transmitting data to the server, which then performs selective analysis only on relevant data points, reducing overall computational load while maintaining accurate vehicle awareness
3Speed
If frequent position updates are transmitted to ensure real-time awareness, then collision detection timeliness is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent implements periodic position updates with adaptive timing based on vehicle dynamics. Instead of continuous transmission, the system updates positions at optimized intervals, reducing network bandwidth consumption while maintaining timely collision detection through strategically timed updates that respond to changes in vehicle speed, direction, and proximity
Data Source
AI summary
A method and system for producing a vulnerable vehicle awareness notification by a server device may include, on a server side: receiving, from a first client device, a location data element, representing current geographical location of the first client device; repeatedly receiving, from one or more second client devices, a corresponding position sample data element associated with a current timestamp; for at least one second client device, identifying a condition of geographical vicinity between the first client device and the at least one second client device; and based on said identified condition, sending the subset of position samples to the first client device. The first client device may, in turn, produce the vulnerable vehicle awareness notification based on the received subset of position samples.