Adaptive Vehicle Following Control Using Surrounding Traffic Count
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Solution Overview
Problem
Existing vehicle control systems struggle to accurately mimic human driving behavior in adjusting distance and speed to the vehicle in front, particularly in complex traffic scenarios, relying on static optimization and lacking real-time adaptability.
Innovation Solution
A vehicle control system that utilizes sensor information to dynamically adjust ego vehicle speed and time gap based on the number of surrounding vehicles, employing a control unit and sensor arrangement to mimic human driving behavior by altering time gaps and distances in response to changing traffic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static optimization is used for distance control, then the system is simple to implement, but it lacks real-time adaptability to changing traffic conditions
Solution Approach 1:
The patent implements dynamic time gap adjustment by continuously monitoring the number of surrounding vehicles and automatically modifying the time gap parameter in real-time based on traffic density, transforming a static control parameter into a dynamic one that adapts to changing conditions
Solution Approach 2:
The system employs feedback mechanisms by detecting the number of surrounding vehicles through sensors and using this information to adjust the time gap parameter, creating a closed-loop control system that responds to real-time traffic conditions
2Measurement precision
If complex machine learning algorithms are used to mimic human behavior, then accuracy of behavior mimicry improves, but the system becomes more complex and requires subjective calibration
Solution Approach 1:
The patent changes the parameter from complex machine learning models to simple rule-based logic that adjusts time gap based on the count of surrounding vehicles, achieving human-like behavior through parameter modification rather than complex computation
Solution Approach 2:
The system replaces expensive and complex machine learning algorithms with simple, computationally inexpensive rule-based logic that achieves the same behavioral mimicry goal without requiring extensive calibration or processing power
Data Source
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AI summary
The present disclosure relates to a vehicle control system (2) comprising a control unit arrangement (3) and at least one sensor arrangement (4, 5) in an ego vehicle (1). The sensor arrangement (4, 5) is adapted to provide sensor information for one preceding target vehicle (6) and surrounding target vehicles (7, 8, 9, 10, 11) separate from the preceding target vehicle (6). The control unit arrangement (3) is adapted to control an ego vehicle speed (v1) in dependence of the sensor information associated with the preceding target vehicle (6) such that an ego distance (r1) between the ego vehicle (1) and the preceding target vehicle (6) is obtained. A time gap (ΔT1) is defined as the time for travelling the ego distance (r1) at the ego vehicle speed (v1), The control unit arrangement (8) is adapted to control the ego vehicle speed (v1) in dependence of the sensor information associated with the surrounding target vehicles (7, 8, 9, 10, 11) such that a present time gap (ΔT1) is maintained in dependence of the number of detected surrounding target vehicles (11).