Abnormal Driving Origin Verification Using Multi-Vehicle Data
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Solution Overview
Problem
Existing autonomous driving systems struggle to accurately determine the origin of abnormal driving behaviors, leading to potential safety risks and inefficiencies due to reliance on potentially noisy sensor data and the inability to distinguish between the ego vehicle and other vehicles' anomalous behaviors.
Innovation Solution
A system is implemented that utilizes Vehicle-to-X communication to verify the origin of abnormal driving by collecting and analyzing data from the ego vehicle and other vehicles, including sensor data, algorithms, and historical patterns, to confirm or deny the identification of abnormal driving through a remote anomaly managing system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anomaly detection is performed using only sensor data from the ego vehicle, then the detection process is simple and fast, but the accuracy is reduced due to noisy or wrong data that may mislead the determination
Solution Approach 1:
A remote anomaly managing system acts as an intermediary between multiple vehicles and the anomaly detection process. This central system collects sensor data from the ego vehicle and second vehicle, performs comprehensive analysis to verify abnormal driving origins, and resolves uncertainties that individual vehicles cannot determine alone, thereby improving detection accuracy without requiring each vehicle to have complex verification capabilities
Solution Approach 2:
The system merges sensor data from multiple sources (ego vehicle and second vehicle) to create a comprehensive view of the driving situation. By combining data from both vehicles and analyzing it centrally, the system overcomes the limitations of single-vehicle sensor data and achieves more accurate determination of abnormal driving origins
2Reliability
If the system relies on single-vehicle sensor data, then the system architecture is simple, but the system cannot distinguish whether the ego vehicle or another vehicle is the origin of abnormal driving
Solution Approach 1:
The remote anomaly managing system serves as a mediator that receives data from multiple vehicles, compares their perspectives, and determines the true origin of abnormal driving. This intermediary approach allows the system to distinguish between ego vehicle and second vehicle as the source of abnormal behavior, improving reliability without requiring complex peer-to-peer verification infrastructure
Solution Approach 2:
The system adds a new dimension to anomaly detection by incorporating spatial and temporal data from multiple vehicles. By analyzing data from different perspectives (ego vehicle's view and second vehicle's view) and combining them in a centralized system, the system can triangulate the origin of abnormal driving, effectively using an additional dimension of information
3Measurement precision
If comprehensive multi-vehicle data is collected to verify abnormal driving, then the accuracy of identifying the origin is improved, but the data processing time and system complexity increase
Solution Approach 1:
Sensor data from vehicles is continuously collected and pre-processed in real-time during normal operation, before anomaly verification is needed. This preliminary data collection and preparation allows the remote anomaly managing system to quickly verify abnormalities when they occur, reducing the time penalty of comprehensive data analysis while maintaining high accuracy
Data Source
AI summary
Systems and methods are provided for programmatically verifying an origin of abnormal driving. Some examples of abnormal driving may include aggressive driving (e.g., tailgating, cut-in lane, etc.), distracted driving (e.g., swerving, delayed reaction, etc.), and reckless driving (e.g., green light running, lane change without signaling, etc.). For example, the systems and methods may receive an identification of a second vehicle performing abnormal driving from an ego vehicle; initiate a verification process of the identification of the abnormal driving; access driving data associated with an origin of the abnormal driving, wherein the driving data includes the ego vehicle and the second vehicle; using the driving data, determine a confirm or deny decision regarding the identification of the second vehicle from the ego vehicle; and provide the confirm or deny decision.