Adaptive Driving Control Apparatus for Real-Time Event Processing
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Solution Overview
Problem
Existing driving control methods face challenges in real-time decision-making due to high processing loads caused by the large number of traffic lines and obstacles, making it difficult to determine accurate driving actions in a timely manner.
Innovation Solution
A method that extracts relevant events based on detection information and creates a driving plan with defined actions for each event, adjusting detection conditions according to the content of these actions to reduce processing load and enhance real-time determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiscale recognition is used to calculate traffic lines of vehicle and obstacle, then measurement precision of traffic lines is improved, but device complexity and processing load increase considerably
Solution Approach 1:
The patent applies segmentation by dividing the complex multiscale recognition process into distinct stages: first identifying candidate traffic lines using simplified criteria, then performing detailed recognition only on selected candidates. This segmentation reduces the overall processing load while maintaining recognition precision for critical traffic lines.
Solution Approach 2:
The patent implements local quality by applying different processing intensities to different regions of interest. High-precision multiscale recognition is applied selectively to areas where traffic lines are most critical (e.g., near intersections or obstacles), while other areas use simplified detection methods, thereby optimizing the balance between precision and computational complexity.
2Stability of the object's composition
If uniform detection conditions are applied to vehicle and obstacle, then detection consistency is improved, but processing load remains constantly high
Solution Approach 1:
The patent applies dynamics by making detection conditions adaptive rather than static. Detection parameters such as search range, resolution, and frequency are dynamically adjusted based on the current driving context, vehicle state, and detected obstacle characteristics. This allows the system to maintain detection consistency for critical elements while reducing processing load during low-risk periods.
Solution Approach 2:
The patent implements parameter changes by varying detection parameters (e.g., detection range, threshold values, sampling frequency) according to the driving situation. For example, detection range expands when obstacles are detected, while it contracts during clear conditions. This dynamic parameter adjustment maintains detection effectiveness while optimizing processing efficiency across different operational scenarios.
Data Source
AI summary
A driving control method is provided in which a processor configured to control driving of a vehicle acquires detection information around a vehicle on the basis of a detection condition that can be set for each point; extracts events which the vehicle encounters, on the basis of the detection information; creates a driving plan in which a driving action is defined for each of the events on the basis of the detection information acquired in the events; executes a driving control instruction for the vehicle in accordance with the driving plan; and determines the detection condition on the basis of the content of the driving action defined for each of the events.