ADAS Control Logic Dynamic Adjustment
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Solution Overview
Problem
Existing advanced driver assistance systems (ADAS) in vehicles struggle to adapt their settings in real-time based on changing environmental conditions, leading to suboptimal performance in responding to dynamic driving scenarios.
Innovation Solution
A vehicle equipped with a sensing unit to acquire environment information and a controller that adjusts ADAS control logic settings based on real-time data, including driving patterns of adjacent vehicles and location information, allowing for dynamic adjustments to systems like Blind Spot Detection, Forward Collision Warning, and Smart Cruise Control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ADAS control logic uses fixed setting values (preset by user or during manufacturing), then system stability and predictability are improved, but the system cannot adapt to changing environmental conditions and driving patterns
Solution Approach 1:
The patent applies dynamics by making the ADAS control logic adjustable and adaptable to changing conditions. The controller dynamically modifies control parameters based on detected driving patterns and environmental information, transforming a static system into one that evolves with usage. This resolves the contradiction by enabling adaptability while maintaining manageable complexity through structured adjustment mechanisms.
Solution Approach 2:
The patent implements parameter changes by modifying control logic settings based on detected driving patterns and environmental conditions. The controller adjusts parameters such as warning thresholds, detection sensitivity, and control thresholds dynamically. This allows the system to adapt to different environments (urban, highway, rural) while keeping the overall system structure intact, balancing adaptability with complexity.
2Reliability
If ADAS control logic is adjusted in real-time based on environment information, then response optimality to changing conditions is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple driving patterns (conservative, moderate, aggressive) and their corresponding control parameters before operation. When a driving pattern is detected, the controller directly applies the pre-prepared parameter set. This approach improves safety through real-time adaptation while avoiding the complexity of calculating optimal parameters from scratch during operation.
Solution Approach 2:
The patent implements feedback by continuously monitoring driving behavior and environmental conditions, comparing detected patterns against predefined patterns, and adjusting control logic accordingly. The sensing unit provides ongoing feedback about surrounding vehicles, road conditions, and driver behavior, enabling the controller to maintain optimal safety performance without requiring overly complex real-time computation.
3Measurement precision
If the system collects and processes extensive environment information (driving patterns, location data, adjacent vehicle information), then adaptability and accuracy of control adjustments are improved, but information processing load and communication requirements increase
Solution Approach 1:
The patent applies taking out by extracting only the essential features needed for driving pattern recognition from the raw sensor data. Instead of processing all available environment information, the controller identifies and processes key parameters such as relative positions of adjacent vehicles, speed differentials, and lane change behaviors. This reduces communication data transmission requirements while maintaining accurate driving pattern detection.
Solution Approach 2:
The patent implements local quality by applying different levels of information processing to different aspects of the environment. For example, adjacent vehicles in critical positions (blind spots, immediate front/rear) receive higher processing priority with more detailed analysis, while distant or less critical objects receive minimal processing. This optimizes measurement precision for safety-critical detections while reducing overall information processing load.
Data Source
AI summary
A vehicle operating in accordance with a predetermined advanced driver assistance system (ADAS) control logic includes a sensing unit configured to acquire environment information of a given area, and a controller configured to change a setting value of the ADAS control logic based on the acquired environment information in real time and control the vehicle in accordance with the ADAS control logic using the changed setting value.