Autonomous Vehicle Routing via Multi-Source Traffic Detection
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Solution Overview
Problem
Current self-driving vehicle systems lack the ability to efficiently adapt to external events and human behavior, leading to inefficiencies in traffic navigation and ride coordination, particularly in high-traffic areas.
Innovation Solution
A vehicle management system that communicates with a remote computing device to monitor traffic conditions and human activity through various data sources, such as social media check-ins, Bluetooth signals, and Wi-Fi signals, allowing it to dynamically adjust pick-up locations to minimize traffic congestion and ensure timely arrivals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If the self-driving vehicle sends the rider to a remote location away from high-traffic areas, then the rider's safety and comfort are improved, but the rider's access to needed services and activities is reduced
Solution Approach 1:
The system performs preliminary identification of high-traffic areas using multiple data sources (social media check-ins, Bluetooth signals, Wi-Fi signals, traffic cameras) before the rider arrives. This advance knowledge allows the vehicle to proactively route around congested zones while still delivering the rider to locations with needed services, resolving the contradiction between safety and accessibility.
Solution Approach 2:
The system continuously monitors traffic conditions, social media check-ins, Bluetooth device detections, and Wi-Fi signal densities in real-time, providing feedback to the routing algorithm. This feedback loop enables dynamic route adjustment that maintains rider safety while ensuring access to services based on current conditions rather than static pre-planned routes.
2Measurement precision
If the vehicle uses multiple data sources to identify high-traffic areas, then the accuracy of traffic detection is improved, but the complexity of the system increases
Solution Approach 1:
The vehicle's computing system serves multiple functions: it processes social media check-ins, analyzes Bluetooth signals, detects Wi-Fi densities, processes traffic camera data, and performs route optimization all through a single multi-functional platform. This universal system approach improves measurement precision across all data types while avoiding the complexity of separate dedicated systems for each function.
Solution Approach 2:
The patent combines multiple previously separate data collection and processing functions into a unified system that simultaneously handles social media data, Bluetooth detections, Wi-Fi signal analysis, and traffic camera processing. This merging reduces overall system complexity by eliminating redundant infrastructure while maintaining high detection accuracy through integrated multi-source analysis.
Data Source
AI summary
Self-driving vehicles have unlimited potential to learn and predict human behavior and perform actions accordingly. Several embodiments described herein include a method of using a vehicle management system to identify a high traffic area and move a self-driving vehicle to meet a rider. Methods also include receiving, by the vehicle management system, a primary pick-up location to meet the rider and detecting, by an antenna of the self-driving vehicle, a number of remote computing devices adjacent the primary pick-up location. Additionally, methods may include determining, by at least one of the vehicle management system and a remote computing device associated with the rider, that the number of remote computing devices exceeds a predetermined remote computing device threshold.