Driver Assistance Prediction for Non-Priority Road Vehicle Entry
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Solution Overview
Problem
Existing driver assistance systems struggle to predict the behavior of vehicles on non-priority roads merging or intersecting with priority roads, especially under the influence of driver mental states such as irritation or impatience, leading to high collision risks.
Innovation Solution
A driver assistance apparatus equipped with a processor that predicts the behavior of a prediction target vehicle by acquiring data on the presence of the vehicle, surrounding vehicles, and available entry spaces, and then estimates waiting time or time to spare, and predicts the possibility of entry based on these factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If driver assistance systems use basic detection methods to monitor vehicles on non-priority roads, then the system complexity is low, but the prediction accuracy of vehicle behavior is insufficient leading to high collision risks
Solution Approach 1:
The system segments the prediction process into multiple independent modules: detection module for acquiring vehicle positions and speeds, waiting time estimation module for calculating expected wait durations, and behavior prediction module for determining likelihood of entering priority road. This segmentation allows each module to specialize in one aspect of the prediction task, improving overall accuracy while keeping individual modules manageable in complexity.
Solution Approach 2:
The system performs preliminary estimation of waiting time before actual collision risk assessment. By calculating the expected waiting time of vehicles on non-priority roads in advance, the system can proactively identify potential collision risks and prepare appropriate warnings or control actions, rather than reacting only when collision becomes imminent.
2Reliability
If the system monitors all vehicles on priority roads to ensure safety, then collision risk is reduced, but the information processing load and response time increase
Solution Approach 1:
The system applies different monitoring intensities to different vehicles based on their local situation. Vehicles on non-priority roads approaching intersections are monitored with higher intensity (calculating waiting time and entry likelihood), while other vehicles receive standard monitoring. This localized focus improves collision avoidance reliability for the most critical scenarios without proportionally increasing processing load for all vehicles.
Solution Approach 2:
The system changes the monitoring parameters dynamically based on vehicle state. When a vehicle is detected on a non-priority road, the system calculates specific parameters such as waiting time estimation and entry likelihood probability. These parameter changes allow the system to process information more efficiently by focusing computational resources on calculating the most relevant predictors of potential collision rather than uniformly processing all vehicle data.
Data Source
AI summary
A driver assistance apparatus includes a processor. The processor acquires first data indicating that a prediction target vehicle is present, that second vehicles are present on one or more lanes ahead of a first vehicle, and that an entry space available for entry of the prediction target vehicle is present, among one or more spaces each formed by any adjacent two of the second vehicles on any common lane. When the prediction target vehicle is waiting at a waiting point on a non-priority road, the processor estimates waiting time for the prediction target vehicle. When the prediction target vehicle is traveling toward the waiting point, the processor estimates time to spare until the entry of the prediction target vehicle into the entry space. The processor predicts possibility of the entry of the prediction target vehicle into the entry space based on the waiting time or the time to spare.