Intersection Risk Indicators Using Near-Miss and Driver Distraction Data
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Solution Overview
Problem
Intersection-related accidents account for a significant percentage of road crashes due to the complexity and hazard of these areas in vehicle transportation networks, necessitating effective risk assessment and management, but existing technologies face challenges in processing and providing meaningful insights from the large volume of collected data.
Innovation Solution
A system and method that determine intersection risk indicators by receiving data from proximate sensors, calculating driver risk scores and near miss scores using machine learning algorithms, and assigning an exponential moving average risk indicator level to facilitate informed decision-making for connected vehicles and traffic management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large volume of transportation network data is collected from sensors and connected vehicles, then the completeness and detail of risk assessment information is improved, but the complexity of processing and extracting meaningful insights deteriorates
Solution Approach 1:
The patent extracts specific risk indicator parameters (TTC, TET, TIT, PET) from the large volume of collected transportation network data. By focusing on these key extracted metrics rather than processing all raw data, the system achieves effective risk assessment while reducing processing complexity. The extraction process selectively isolates meaningful risk-related information from the overwhelming data stream.
Solution Approach 2:
The patent introduces risk indicator calculations as intermediary metrics that mediate between raw sensor data and safety decisions. These risk indicators (TTC, TET, TIT, PET) serve as intermediate representations that simplify the complex relationship between multiple sensor inputs and the final risk assessment, making the data more manageable and interpretable.
2Measurement precision
If multiple risk indicators (TTC, TET, TIT, PET) are calculated and monitored, then the precision of intersection risk assessment is improved, but the computational resources and processing time required deteriorates
Solution Approach 1:
The patent calculates risk indicators in advance as vehicles approach intersections, using predicted trajectories and pre-computed risk metrics. By performing preliminary risk assessments before vehicles reach critical zones, the system prepares risk information ahead of time, reducing last-minute computational demands and enabling faster real-time decision-making at intersections.
Solution Approach 2:
The patent implements dynamic updating of risk indicators based on real-time vehicle positions and changing traffic conditions. Rather than continuously recalculating all indicators at maximum frequency, the system dynamically adjusts calculation intervals based on vehicle proximity to intersections and current risk levels, optimizing the balance between assessment precision and processing efficiency.
Data Source
AI summary
A method and system for determining risk indicators for intersections. A method includes receiving, by a computing node from intersection proximate sensors at each intersection, intersection data. For each intersection, the method includes determining, by the computing node for each connected vehicle proximate to an intersection, a driver risk score based on driver distraction data from the intersection data for the intersection, determining, by the computing node, a near miss score based on the intersection data for the intersection, and assigning, by the computing node for the intersection, an intersection risk indicator level based on an exponential moving average of intersection risk indicator scores determined from driver risk scores and near miss scores. The method includes providing, by the computing node, intersection risk indicator levels to each connected vehicle to facilitate control decisions by each connected vehicle when approaching intersections.