Edge Driving Behavior Scoring Using Road User And Infrastructure Data
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Solution Overview
Problem
Conventional methods for monitoring and quantifying vehicle behavior are insufficient as they do not consider interactions with other vehicles and road infrastructure, leading to incomplete and non-robust driving behavior assessments that may overlook adverse impacts on the environment and other road users.
Innovation Solution
An edge computing device analyzes vehicle behavior by receiving data from vehicles, remote vehicles, road users, and infrastructure devices to calculate a vehicle behavior score, comparing it to a threshold, and generating control signals for adjustments when necessary, reducing latency and processing demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional vehicle behavior monitoring methods are used, then the system complexity is low, but the measurement precision and reliability of driving behavior assessment are insufficient
Solution Approach 1:
The patent combines multiple data sources (vehicle sensors, remote vehicles, road users, and road infrastructure devices) into a unified behavior scoring system. The edge computing device aggregates data from these diverse sources to calculate comprehensive vehicle behavior scores, transforming individual isolated monitoring systems into an integrated ecosystem that achieves higher measurement precision through multi-source validation and correlation.
2Reliability
If real-time vehicle behavior analysis with multiple data sources is implemented, then the reliability of behavior scoring is improved, but the data transmission and processing demands increase
Solution Approach 1:
The edge computing device serves as an intermediary between multiple data sources and the central cloud infrastructure. It receives, processes, and validates data from vehicles, road users, and infrastructure devices locally, performing preliminary behavior score calculations and filtering operations before transmitting consolidated information to the cloud. This intermediary role reduces the volume of raw data that needs long-distance transmission and enables real-time reliability verification without overwhelming the network.
3Measurement precision
If comprehensive multi-source data processing is performed in the cloud, then the measurement precision of vehicle behavior assessment is improved, but the latency increases
Solution Approach 1:
The patent segments the behavior analysis processing into two distinct layers: real-time critical processing at the edge computing device and comprehensive long-term analysis in the cloud. The edge device handles time-sensitive tasks such as immediate behavior score calculation, real-time threshold comparisons, and urgent control signal generation. The cloud handles less time-critical functions like historical pattern recognition and model training. This segmentation enables precision through comprehensive analysis while minimizing latency for critical operations.
4Reliability
If vehicle behavior monitoring considers only vehicle condition, then the device complexity is low, but the reliability and completeness of behavior assessment deteriorates
Solution Approach 1:
The monitoring system is designed with multi-functionality to serve multiple purposes simultaneously. The same data collection infrastructure supports not only vehicle behavior scoring but also road infrastructure monitoring, remote vehicle tracking, and road user behavior analysis. The edge computing device performs multiple functions including data aggregation from diverse sources, real-time behavior score calculation, threshold comparison, control signal generation, and historical data management. This universal approach increases assessment reliability without proportionally increasing complexity.
Data Source
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AI summary
The disclosure herein pertains to monitoring and quantifying vehicle drive behavior using an edge computing device. In one example, a method may include receiving vehicle data from a vehicle; receiving remote vehicle data from a remote vehicle; receiving road user data from a road user; receiving road infrastructure data from a road infrastructure device; calculating a vehicle behavior score of the vehicle using vehicle data and one or more of remote vehicle data, road user data, and road infrastructure data; and outputting and storing the vehicle behavior score.