Digital Twin Traffic Anomaly Reconstruction
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Solution Overview
Problem
In traffic accidents, especially in crowded areas, identifying the perpetrator can be challenging due to unclear legal situations and the difficulty in finding and verifying witness information, which is exacerbated by the introduction of autonomous vehicles reducing human driver witnesses.
Innovation Solution
A digital twin system that reconstructs traffic anomaly scenes by identifying and requesting information from potential witnesses and traffic actors using wireless proximity estimation and sensor data, while ensuring privacy through anonymous identifiers and data fusion techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If witness search methods are used in traffic accidents, then information about the incident can be obtained, but the search process is arduous and time-consuming
Solution Approach 1:
The system performs preliminary actions by continuously collecting sensor data from traffic actors before the accident occurs and automatically identifying potential witnesses based on pre-stored proximity information. This preliminary preparation eliminates the need for manual witness searching after the incident, significantly reducing investigation time while ensuring complete information capture.
Solution Approach 2:
The patent creates a digital twin copy of the physical traffic scene that includes virtual representations of all traffic actors, their sensor data, and reconstructed event information. This digital copy serves as a complete record that can be analyzed without requiring physical witness location, thereby obtaining full incident information instantly without time loss.
2Loss of information
If human driver witnesses are relied upon, then testimony can be obtained, but the number of potential witnesses decreases with autonomous vehicles
Solution Approach 1:
The patent replaces the mechanical system of human witness testimony with an automated sensor-based information collection system. Traffic actors equipped with sensors (cameras, lidar, radar) automatically capture and transmit data about the incident, eliminating dependency on human witnesses while increasing the quantity and reliability of available information through multiple sensor perspectives.
Solution Approach 2:
The system enables traffic actors to self-report incident information through their onboard sensors and communication systems. Each traffic actor automatically detects anomalies, captures relevant data, and transmits it to the reconstruction system without requiring external witness intervention, thereby maintaining information collection capability even when human witnesses are absent.
3Measurement precision
If witness information is collected, then the accuracy of incident reconstruction can be improved, but privacy concerns arise
Solution Approach 1:
The patent applies local quality by collecting and processing sensor data only from traffic actors within the specific spatial and temporal context of the incident. The system identifies relevant witnesses and data based on local proximity to the event location and time, ensuring that only necessary information is collected from nearby actors while excluding data from distant or unrelated sources, thereby maintaining privacy boundaries.
Solution Approach 2:
The system introduces a central reconstruction system as an intermediary that receives sensor data from multiple traffic actors, processes this information through digital twin modeling, and generates the final incident reconstruction. This intermediary approach allows accurate reconstruction while maintaining privacy, as the system aggregates and processes data centrally without directly accessing or exposing individual actor information.
Data Source
AI summary
An apparatus, including: an interface operable to receive from traffic actors, information related to a traffic anomaly in a traffic scene; processing circuitry operable to: generate a digital twin of the traffic scene, wherein the digital twin is a virtual representation of the traffic scene; fuse the received traffic anomaly information; and reconstruct the traffic scene by the digital twin incorporating the fused traffic anomaly information.