Abnormal Vehicle Route Prediction Using Historical Driving Data
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Solution Overview
Problem
Conventional systems for alerting nearby vehicles to an abnormal vehicle's condition lack accuracy due to insufficient driving information, such as gas pedal, brake, and steering wheel data, leading to potential mistakes in avoiding collisions.
Innovation Solution
A system that predicts the traveling route of an abnormal vehicle by acquiring historical data and vehicle information, including operating statuses, to compute collision risk values and provide recommended routes with lower risk to nearby vehicles, using a database categorized by similarity and incorporating real-time vehicle data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional systems transmit malfunction messages to nearby vehicles, then nearby vehicles can receive abnormal vehicle information, but the information is insufficient for accurate collision avoidance due to lack of detailed driving data
Solution Approach 1:
The system performs preliminary action by acquiring and storing detailed driving information (gas pedal, brake, steering wheel data) before a collision occurs. This historical driving data is collected and processed in advance, enabling accurate prediction of the abnormal vehicle's future route and more reliable collision avoidance decisions when the malfunction occurs.
2Measurement precision
If the system acquires detailed vehicle information including gas pedal, brake, and steering wheel data, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system applies universality by using a multi-functional avoidance system that handles multiple tasks: acquiring driving information, storing historical data, predicting abnormal vehicle routes, calculating collision risks, and providing avoidance recommendations. This consolidates numerous functions into a single integrated system, improving measurement precision without proportionally increasing device complexity.
3Reliability
If the system continuously monitors and re-computes collision risk values, then collision avoidance reliability improves, but computational time and energy consumption increase
Solution Approach 1:
The system implements periodic action by re-computing collision risk values at regular time intervals rather than continuously. The avoidance system periodically updates the abnormal vehicle's predicted route and recalculates collision risks based on new driving information, maintaining high reliability while reducing computational time and energy consumption compared to continuous monitoring.
Data Source
AI summary
The disclosure is related to a system and a method for avoiding abnormal vehicle. In the method, the avoidance system predicts multiple routes for the abnormal vehicle within a period of time according to historical data when an alert from the abnormal vehicle is generated. A route-potential figure can be created when the system gets the historical data. The system computes one or more available routes for the nearby vehicle based on its vehicle information when a collision is possible. Every available route has its collision risk value. The system finally provides a recommended route with lower collision risk value when it considers a time of the abnormal vehicle reaches its great change, a time of predicting the nearby vehicle meets the range of route-potential figure, and a safety distance there-between.