Virtual Boundary Maneuver Prediction for Vehicle Collision Avoidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle systems lack an efficient method to predict and respond to the maneuvers of target vehicles approaching virtual boundaries, which limits their ability to safely adjust paths and avoid collisions.
Innovation Solution
A system that uses sensors to detect target vehicles and identify virtual boundaries, determining constraint values based on approach velocities and accelerations, and adjusts the host vehicle's path by actuating propulsion, steering, or braking to avoid collisions, utilizing a path planning algorithm and minimizing time differences between predicted and actual maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses traditional methods to detect and respond to target vehicles, then it can identify target positions and speeds, but it lacks the ability to efficiently predict maneuvers and adjust paths timely
Solution Approach 1:
The system performs preliminary actions by establishing virtual boundaries around the target vehicle and continuously monitoring constraint values (approach velocity and acceleration) before the host vehicle reaches critical proximity. This allows the system to predict potential maneuvers and adjust the path in advance, rather than reacting only when the target is immediately threatening, thereby improving collision avoidance while maintaining adequate response time.
2Measurement precision
If the system monitors target vehicle approach in detail to improve prediction accuracy, then it can better identify maneuvers, but it increases computational complexity and data processing requirements
Solution Approach 1:
The system extracts only the essential parameters needed for maneuver prediction by establishing virtual boundaries and calculating constraint values (approach velocity and acceleration) relative to these boundaries. This selective extraction of critical information allows the system to achieve accurate maneuver prediction without processing all possible target vehicle data, thereby reducing computational complexity while maintaining prediction accuracy.
3Reliability
If the system adjusts the host vehicle path frequently to avoid collisions, then it improves safety, but it may cause unnecessary maneuvers and reduce driving efficiency
Solution Approach 1:
The system employs feedback by continuously monitoring constraint values (approach velocity and acceleration) and comparing them against thresholds to determine when path adjustment is necessary. This feedback mechanism allows the system to make path adjustments only when actual maneuver predictions indicate a genuine collision risk, rather than making frequent preemptive adjustments, thereby improving safety while minimizing unnecessary maneuvers that would reduce driving efficiency.
Data Source
AI summary
A computer is programmed to identify a target vehicle to be monitored and to identify first and second virtual boundaries on a roadway based on a position of the target vehicle. The computer is further programmed to determine a first constraint value based on (1) a first boundary approach velocity and (2) a first boundary approach acceleration and a second constraint value based on (1) a second boundary approach velocity and (2) a second boundary approach acceleration. The computer is further programmed to identify a maneuver of the target vehicle based on whether the first and second constraint values violate respective thresholds or a position of the target vehicle relative to the first and second virtual boundaries violates a threshold and to adjust a path of a host vehicle according to the identified maneuver.