Driving Assistance Collision Prediction for Unidentified Intersections
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Solution Overview
Problem
Existing driving assistance devices fail to provide effective collision prevention when an intersection is not identified as a risk position, even if it is in the traveling direction of the vehicle.
Innovation Solution
A driving assistance device that includes a storage unit for risk position information, an acquisition unit for peripheral vehicle data, a prediction unit to assess collision likelihood based on vehicle speed, position, and yaw rate, and a notification unit to alert the occupant, determining evaluation distances and predicted tracks to prevent collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving assistance is performed only when risk position is identified, then false notifications are reduced, but collision risks at unidentified intersections are missed
Solution Approach 1:
The system performs preliminary collision risk assessment by predicting future positions and tracks of both self-vehicle and peripheral vehicles before actual collision occurs. This allows the system to identify potential risks at intersections even when traditional risk position identification fails, by evaluating whether predicted tracks will intersect within a predetermined distance range.
Solution Approach 2:
The system dynamically adjusts the collision risk assessment based on real-time vehicle information including position, speed, and yaw rate. By continuously updating predicted tracks and evaluation distances as vehicles move, the system can adaptively identify collision risks at varying intersection scenarios without relying on static risk position databases.
2Productivity
If driving assistance is performed without risk position identification, then more intersections are covered, but false notifications increase
Solution Approach 1:
The system replaces the traditional mechanical approach of relying on pre-identified risk position databases with a dynamic prediction mechanism. Instead of checking whether an intersection exists in a database, the system calculates predicted vehicle tracks and evaluates whether they will intersect within a predetermined distance, substituting static data lookup with dynamic geometric calculation.
Solution Approach 2:
The system changes the assessment parameters from static risk position identification to dynamic track intersection evaluation. By using vehicle motion parameters (position, speed, yaw rate) to calculate future trajectories and evaluation distances, the system transforms the collision risk assessment from a location-based problem to a motion-based prediction problem.
3Measurement precision
If predicted tracks are calculated for all peripheral vehicles, then collision detection accuracy improves, but computational load increases
Solution Approach 1:
The system applies local quality by focusing computational resources on evaluating only those peripheral vehicles whose predicted tracks may intersect with the self-vehicle's track within the predetermined distance range. Instead of uniformly processing all peripheral vehicles, the system selectively intensifies evaluation for vehicles in critical proximity zones while simplifying or skipping evaluation for distant vehicles.
Solution Approach 2:
The system performs partial action by calculating predicted tracks and evaluation distances only for the portion of the driving environment that requires detailed assessment (vehicles within critical distance ranges). For vehicles clearly outside the risk zone, the system performs minimal or no track prediction, reducing overall computational load while maintaining sufficient accuracy for collision prevention.
Data Source
AI summary
A driving assistance device is configured to predict a possibility of collision between a self-vehicle and a peripheral vehicle based on self-vehicle information and the peripheral vehicle information and notify an occupant of the self-vehicle based on a prediction result. In a case where a risk position is not included within a predetermined distance in a traveling direction of the self-vehicle, the device determines predicted tracks of the self-vehicle and the peripheral vehicle based on the self-vehicle information and the peripheral vehicle information, determines an evaluation distance for evaluating an approach situation between the self-vehicle and the peripheral vehicle based on the predicted tracks, and predicts that there is no possibility of collision between the self-vehicle and the peripheral vehicle when the evaluation distance is not included in a predetermined range.