Remote Vehicle Hazard Scoring for Erratic Driver Detection
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Solution Overview
Problem
Current collision avoidance systems fail to detect collision risks caused by unexpected driver actions and erratic behaviors, as well as damaged vehicle components.
Innovation Solution
A system equipped with a vehicle sensor, GNSS, and a controller that determines classification scores for a remote vehicle based on measurements, including speed, position, and environmental factors, to calculate an overall hazard score and take appropriate actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current collision avoidance systems use sensors to detect objects in the environment, then they can prevent and reduce the severity of vehicular collisions with remote vehicles, pedestrians, and structures, but they fail to detect collision risks caused by unexpected driver actions and erratic driving behaviors
Solution Approach 1:
The system segments the hazard detection task into multiple classification scores: position and speed violation scores, mutual interaction violation scores, anomaly detection scores, traffic rule violation scores, and visual hazard scores. Each score evaluates a specific aspect of remote vehicle behavior, allowing the system to comprehensively detect both normal and erratic driving patterns that single-metric systems miss.
Solution Approach 2:
The system transitions from traditional single-metric collision detection to a multi-dimensional evaluation framework by calculating multiple classification scores simultaneously. This dimensional expansion enables detection of subtle erratic behaviors across different dimensions (position, speed, interactions, anomalies, visual hazards) that individual metrics cannot capture alone.
2Measurement precision
If the system calculates multiple classification scores including position violations, speed violations, mutual interaction violations, anomaly detection, traffic rule violations, and visual hazards, then it improves detection accuracy of hazardous vehicles, but it increases computational complexity and processing time
Solution Approach 1:
The complex hazard detection problem is segmented into six distinct classification score calculations, each focusing on a specific hazard dimension. This segmentation allows the system to process different aspects of vehicle behavior independently and combine results, making the overall complex task manageable through modular computation.
Solution Approach 2:
The controller performs multiple functions simultaneously: it monitors position, speed, interactions, anomalies, traffic rules, and visual hazards all through one integrated hazard scoring system. This multi-functional approach consolidates what would otherwise require separate detection systems into a single unified controller, reducing overall system complexity despite the multiple metrics evaluated.
3Measurement precision
If the system determines classification scores based on multiple factors including road characteristics, road conditions, and hazard statuses, then it improves the accuracy of hazard assessment, but it increases the time required to process and analyze data
Solution Approach 1:
The system determines acceptable speed ranges and evaluates road characteristics, conditions, and hazard statuses in advance before final hazard scoring. By pre-processing environmental context data and establishing baseline safety parameters, the system reduces real-time computation requirements during critical hazard assessment moments, balancing accuracy with processing speed.
Data Source
AI summary
A system for detecting hazards for a vehicle includes a vehicle sensor for determining information about an environment surrounding the vehicle and a global navigation satellite system (GNSS). The system also includes a controller in electrical communication with the vehicle sensor and the GNSS. The controller is programmed to perform a plurality of measurements. The controller is further programmed to determine a plurality of classification scores of the first remote vehicle based at least in part on the plurality of measurements of the first remote vehicle and to determine an overall hazard score of the first remote vehicle based at least in part on the plurality of classification scores of the first remote vehicle. The controller is further programmed to take an action based at least in part on the overall hazard score of the first remote vehicle.