Host Vehicle Stop Position Control for Predicted Path Crossings
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Solution Overview
Problem
Existing vehicle control systems face challenges in accurately predicting the future location of target vehicles and adjusting the path of host vehicles to avoid intersections, particularly in scenarios where target vehicles are departing a sub-area and moving towards the host vehicle's path.
Innovation Solution
A vehicle computer system that identifies target vehicles based on visual cues, predicts future intersections, and determines stop positions or updates the host vehicle's path to prevent collisions by using sensor data, machine learning, and communication protocols for autonomous or semi-autonomous operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses sensor data and machine learning to predict future locations of target vehicles, then collision avoidance capability is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary prediction of target vehicle future locations using sensor data and machine learning algorithms before actual intersection occurs. This advance prediction allows the host vehicle to adjust its path proactively, improving collision avoidance reliability while managing system complexity through early intervention rather than reactive measures
Solution Approach 2:
The system dynamically adjusts the host vehicle's path based on real-time predictions of target vehicle locations. The path adjustment is not static but adapts continuously as new sensor data arrives and predictions are updated, allowing the system to maintain optimal collision avoidance strategies while managing computational complexity through incremental updates
2Reliability
If the system determines stop positions to prevent path intersections, then safety is improved, but travel time increases
Solution Approach 1:
The system applies partial stopping or path adjustment only when and where necessary to prevent intersections, rather than implementing complete stops or excessive path deviations. This selective application of safety measures maintains collision prevention while minimizing unnecessary travel time loss
Solution Approach 2:
The system continuously monitors the relative positions of host and target vehicles, providing feedback that allows real-time adjustment of stop positions and path modifications. This feedback mechanism ensures safety is maintained while optimizing travel time by making only the minimum necessary adjustments
3Reliability
If the system updates the host vehicle path in real-time based on target vehicle movement, then collision prevention is improved, but control complexity increases
Solution Approach 1:
The system establishes preliminary path adjustment strategies based on predicted target vehicle trajectories before actual intersections occur. This advance planning simplifies real-time control by having pre-computed adjustment options ready, reducing the complexity of moment-to-moment decision-making while maintaining effective collision prevention
Solution Approach 2:
The path update mechanism dynamically adapts to target vehicle movement while maintaining a manageable control structure. The system updates paths in real-time but uses structured algorithms that balance responsiveness with computational efficiency, preventing control complexity from becoming unmanageable
Data Source
AI summary
While operating a host vehicle in an area, a target vehicle is identified in a sub-area based on detecting a visual cue from the target vehicle. A future location of the target vehicle is predicted to intersect a path of the host vehicle. A stop position of the host vehicle is determined based on the future location of the target vehicle. At least one of the target vehicle is determined to have moved or the path of the host vehicle is updated. Then, the host vehicle is operated to the stop position or along the updated path.