Vehicle Collision Avoidance via Historical Route Prediction
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Solution Overview
Problem
Conventional collision avoidance systems fail to provide accurate and advanced estimation of an abnormal vehicle's route due to lack of detailed driving information such as gas pedal, brake, and steering wheel operation, leading to potential accidents.
Innovation Solution
A system that predicts the traveling route of an abnormal vehicle by incorporating historical data and vehicle information, including operating statuses of the gas pedal, brake, and steering wheel, to compute collision risk values and recommend safer routes for nearby vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional technology transmits malfunction message to nearby vehicles, then nearby vehicles can receive alerting information, but the estimation of abnormal vehicle's route is inaccurate due to lack of detailed driving information
Solution Approach 1:
The system performs preliminary action by collecting and storing detailed driving information (gas pedal, brake, steering wheel operations) before the abnormal vehicle actually malfunctions. This historical driving data is preserved in advance, enabling accurate route prediction when the malfunction occurs, rather than relying on rough real-time information only.
Solution Approach 2:
The system introduces an intermediary mechanism - a communication system that transmits not only the malfunction alert but also the accumulated historical driving information from the abnormal vehicle to nearby vehicles. This intermediary channel enables the transfer of detailed behavioral data that bridges the gap between the abnormal vehicle and nearby vehicles, allowing precise route estimation.
2Measurement precision
If the system collects detailed driving information from abnormal vehicles, then route prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The system applies universality by designing a multi-functional platform that simultaneously performs multiple tasks: collecting driving information, storing historical data, detecting malfunctions, predicting routes, and communicating with nearby vehicles. This integrated approach consolidates what could be separate complex systems into a single unified system, reducing overall complexity while maintaining high prediction accuracy.
Solution Approach 2:
The system implements self-service by automatically collecting, processing, and analyzing driving information without requiring manual intervention. The abnormal vehicle's own driving data is autonomously captured and used for prediction, and the system automatically generates and transmits alerts to nearby vehicles, reducing operational complexity.
3Reliability
If the system provides real-time route recommendations to nearby vehicles, then collision avoidance effectiveness improves, but the response time required increases
Solution Approach 1:
The system performs preliminary action by pre-calculating potential route predictions based on historical driving patterns before a collision threat actually materializes. When a malfunction is detected, the system can immediately provide pre-prepared route recommendations to nearby vehicles, significantly reducing the response time needed at the critical moment while maintaining high reliability.
Solution Approach 2:
The system applies dynamics by continuously updating route predictions as new driving information becomes available and as the situation evolves. The route recommendations are not static but dynamically adjusted based on real-time conditions, allowing the system to provide accurate guidance while adapting quickly to changing circumstances, thus balancing reliability and response time.
Data Source
AI summary
The disclosure is related to a system and a method for collision avoidance for vehicle. In the method, the system predicts multiple routes of an abnormal vehicle in a period of time according to historical data when a nearby vehicle receives an alert from the abnormal vehicle. A route potential pattern can be created when the system gets the historical data. The system also computes one or more available routes for the nearby vehicle based on its vehicle information. 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 pattern, and a safe distance there-between.