Centralized Traffic Situation Determination Using Multi-Vehicle Sensor Data
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Solution Overview
Problem
Current motor vehicle systems require multiple sensors and complex processing to determine traffic situations, which are not widely available in standard vehicles, limiting comprehensive coverage of road systems.
Innovation Solution
A method where motor vehicles with limited sensors and processing capacity scan their surroundings, transmit data to a central office, and combine information from multiple sources using various sensors and algorithms to determine traffic situations, enabling more accurate and reliable assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors and complex processing are used to determine traffic situations, then measurement precision and reliability are improved, but device complexity and cost increase significantly
Solution Approach 1:
The system divides the traffic situation determination task into two segments: individual vehicles perform local scanning and assumption determination, while the central office performs comprehensive traffic situation determination by combining data from multiple vehicles. This segmentation allows standard vehicles with limited sensors to contribute to accurate traffic situation determination without requiring each vehicle to have complex sensor systems.
Solution Approach 2:
The system merges sensor data from multiple vehicles at the central office to determine comprehensive traffic situations. By combining information from multiple sources, the system achieves high measurement precision without requiring any single vehicle to have complex sensor equipment.
2Reliability
If comprehensive sensor equipment is installed in all vehicles, then traffic situation coverage is improved, but manufacturing cost and device complexity increase
Solution Approach 1:
The system segments the data collection and processing functions between individual vehicles and the central office. Standard vehicles with basic sensors perform local scanning, while the central office aggregates data to achieve comprehensive coverage. This allows widespread participation without requiring expensive equipment in each vehicle.
Solution Approach 2:
Each vehicle performs self-service by scanning its own surroundings and transmitting data to the central office. This decentralized approach enables comprehensive traffic situation coverage using simple sensors in standard vehicles, eliminating the need for expensive comprehensive sensor suites in each vehicle.
3Measurement precision
If more sensor data is processed on individual vehicles, then traffic situation accuracy is improved, but processing time and energy consumption increase
Solution Approach 1:
The system extracts the computationally intensive task of comprehensive traffic situation determination from individual vehicles and relocates it to the central office. Vehicles only perform lightweight local scanning and assumption determination, while the central office handles the complex processing of combined data from multiple sources, reducing processing time and energy consumption at the vehicle level.
4Device complexity
If standard vehicles with limited sensors are used, then device complexity is reduced, but measurement precision and information completeness deteriorate
Solution Approach 1:
The system merges data from multiple vehicles with limited sensors to achieve comprehensive and accurate traffic situation determination. The central office combines information from multiple sources, compensating for the limitations of individual vehicle sensors and achieving high measurement precision without requiring complex sensor systems in each vehicle.
Solution Approach 2:
The system transitions from a single-vehicle perspective to a multi-vehicle network perspective. By aggregating data from multiple vehicles observing different aspects of the traffic situation, the system achieves comprehensive coverage and high accuracy that no single vehicle could achieve alone, effectively adding a spatial dimension to the measurement capability.
Data Source
AI summary
A method for controlling a third motor vehicle, includes scanning, by a first motor vehicle, the surroundings of the first motor vehicle and determining an assumption for a traffic situation based on the scanned information; transmitting the assumption thus determined from the first motor vehicle to a central office, scanning, by a second motor vehicle, the surroundings of the second motor vehicle and determining an assumption for a traffic situation based on the scanned information, transmitting the assumption thus determined from the second motor vehicle to a central office, whereupon the surroundings of the first and a the second motor vehicle overlap, determining, by the central office, a traffic situation in the area of the first and second motor vehicles on the basis of the transmitted information and transmitting the traffic situation thus determined to a third motor vehicle to enable prompt consideration of the traffic situation thus determined.