Pedestrian Navigation via Vehicular Collaborative Computing
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Solution Overview
Problem
Pedestrians face difficulties crossing roadways, particularly in adverse weather or environmental conditions, due to the lack of effective navigation systems that enhance their safety.
Innovation Solution
A pedestrian navigation system utilizing collaborative computing among connected vehicles to analyze pedestrian data and sensor data, predict potential collisions, and provide modified walking paths to reduce risk, transmitted to pedestrian devices through V2X communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional navigation systems are used for pedestrians, then the system complexity is low, but the safety and effectiveness of pedestrian navigation is insufficient
Solution Approach 1:
The patent merges multiple vehicular computing resources into a collaborative computing system that functions as a virtual pedestrian navigation system. Connected vehicles share sensor data and computational capabilities through V2X communication, creating a distributed system that provides advanced pedestrian safety features without requiring complex dedicated pedestrian navigation hardware.
Solution Approach 2:
The patent enables connected vehicles to perform multiple functions: their existing sensor systems detect pedestrians, their computing resources analyze pedestrian safety risks, and their communication systems transmit navigation assistance. This multi-functional approach leverages existing vehicular infrastructure for pedestrian navigation without requiring specialized pedestrian-only equipment.
2Measurement precision
If collaborative computing among connected vehicles is implemented, then pedestrian safety and navigation accuracy are improved, but the computational resource requirements and system complexity increase
Solution Approach 1:
The patent segments the collaborative computing tasks among multiple connected vehicles based on their capabilities and current states. Different vehicles perform different computational functions (data collection, analysis, prediction, communication), distributing the computational load and energy consumption across the vehicular network rather than concentrating it in a single system.
Solution Approach 2:
The patent implements partial computing by having each vehicle perform only the computational tasks necessary for its specific role in the collaborative system. Vehicles contribute their excess computational resources selectively, performing analysis and prediction only when and where needed, rather than continuously maximizing resource usage.
3Reliability
If real-time analysis of pedestrian data and sensor data is performed, then collision risk prediction accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The patent implements preliminary action by having connected vehicles continuously collect and pre-process sensor data about their environment, including potential pedestrian areas, before actual pedestrian navigation assistance is needed. This pre-computation of environmental models and risk zones enables faster real-time responses when pedestrians require navigation assistance.
Solution Approach 2:
The patent employs feedback mechanisms where the collaborative computing system continuously monitors pedestrian movement, updates collision risk assessments, and refines navigation recommendations in real-time. This closed-loop approach improves prediction reliability through iterative analysis while optimizing processing time by focusing computational resources on dynamically changing risk factors.
Data Source
AI summary
The disclosure includes embodiments for pedestrian navigation by a group of connected vehicles executing a collaborative computing process. In some embodiments, a method includes analyzing pedestrian data generated by a pedestrian device and sensor data generated by the group of connected vehicles to determine digital twin data from a set that correlates with a scenario described by the pedestrian data and the sensor data. The digital twin data is an output of a historical digital twin simulation. The method includes predicting, based on the digital twin data, that the pedestrian is at risk of a collision. The method includes determining modified path data describing a modified walking path for the pedestrian. The method includes transmitting the modified path data to the pedestrian device so that the pedestrian is informed about the modified walking path and the risk is modified.