V2X Incident Risk Prediction via ML and Cellular Data
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Solution Overview
Problem
Existing vehicular incident risk prediction systems are reactive and lack accuracy due to reliance on fixed sensors with low frequency and low granularity data, failing to provide real-time, multi-factorial assessments of incident risks to vehicle operators.
Innovation Solution
A machine learning model trained with real-time V2X data and third-party data to assess vehicular incident risk, using cellular V2X communication for continuous data collection and providing in-vehicle notifications when risk thresholds are met, offering a proactive and accurate assessment of potential incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed sensors with low frequency data collection are used, then device complexity is reduced, but measurement precision and reliability of incident risk assessment deteriorate
Solution Approach 1:
The patent replaces fixed mechanical sensors with mobile cellular V2X communication systems. Instead of using static sensor infrastructure that collects data at low frequencies, the system utilizes vehicles' onboard cellular communication capabilities to continuously transmit and receive traffic data, achieving high-frequency data collection without deploying complex fixed sensor networks.
Solution Approach 2:
The system leverages the vehicles' own communication infrastructure (cellular modems and V2X capabilities) to collect and transmit traffic data. Each vehicle serves as both a data collector and a communication node, eliminating the need for separate fixed sensing infrastructure and reducing overall system complexity while maintaining high data frequency.
2Device complexity
If fixed sensors are used for data collection, then device complexity is reduced, but the granularity and real-time capability of incident risk assessment deteriorate
Solution Approach 1:
The patent replaces fixed mechanical sensors with mobile cellular V2X communication systems. Instead of using static sensor infrastructure that collects data at low frequencies, the system utilizes vehicles' onboard cellular communication capabilities to continuously transmit and receive traffic data, achieving high-frequency data collection without deploying complex fixed sensor networks.
Solution Approach 2:
The system transitions from static fixed sensors to dynamic mobile data collection through vehicles. The data collection points move with traffic flow, providing continuous spatial coverage and high-frequency updates as vehicles traverse different locations, thereby capturing fine-grained temporal and spatial variations in traffic conditions.
3Device complexity
If reactive incident prediction systems are used, then system complexity is reduced, but reliability and safety effectiveness deteriorate
Solution Approach 1:
The system performs preliminary risk assessment by continuously analyzing V2X data in real-time to identify potential incident risks before they materialize. The machine learning model evaluates multiple risk factors proactively, enabling early warnings and preventive actions rather than merely reacting to incidents after they occur.
Solution Approach 2:
The system implements continuous feedback loops where V2X data is constantly collected, analyzed by machine learning models, and used to update risk assessments in real-time. This feedback mechanism enables the system to adapt to changing traffic conditions and maintain high reliability in incident prediction by continuously learning from new data.
Data Source
AI summary
Systems, methods, and computer-readable media are described for performing real-time vehicular incident risk prediction using real-time vehicle-to-everything (V2X) data. A vehicular incident risk prediction machine learning model is trained using historical V2X data such as historical incident data and historical vehicle operator driving pattern behavior data as well as third-party data such as environmental condition data and infrastructure condition data. The trained machine learning model is then used to predict the risk of an incident for a vehicle on a roadway segment based on real-time V2X data relating to the roadway segment and/or vehicle operators on the roadway segment. A notification of a high risk of incident can then be sent to a V2X communication device of the vehicle to inform an operator of the vehicle.