Adaptive Vehicle Speed Control Using Surrounding Traffic Time Gaps
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Solution Overview
Problem
Existing vehicle control systems struggle to accurately mimic human driving behavior in adjusting distance and speed to preceding and surrounding vehicles, particularly in complex traffic scenarios, often relying on complicated methods like machine-learning.
Innovation Solution
A vehicle control system that utilizes a control unit and sensor arrangement to provide sensor information for preceding and surrounding vehicles, controlling ego vehicle speed based on the number of detected surrounding vehicles, employing a 2D look-up table and dynamic adjustment algorithm to maintain a time gap that mimics human driving behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning methods are used to adjust distance and speed to mimic human driving behavior, then the accuracy of mimicking human behavior is improved, but the device complexity increases
Solution Approach 1:
The patent copies human driving behavior patterns by observing and replicating how human drivers adjust time gaps based on surrounding traffic conditions. Instead of using complex machine-learning algorithms to learn human behavior, the system directly implements simplified rules that mirror human decision-making: decreasing time gap when surrounding vehicles increase and increasing time gap when surrounding vehicles decrease, thereby achieving accurate human-like driving behavior without complex computational models
Solution Approach 2:
The system changes the parameter of time gap dynamically based on the number of detected surrounding target vehicles. By adjusting this single critical parameter (time gap) in response to traffic conditions, the system achieves accurate mimicry of human driving behavior without requiring complex multi-parameter machine-learning models, thus resolving the contradiction between accuracy and complexity
2Measurement precision
If the time gap is decreased with increased number of surrounding target vehicles to mimic human behavior, then the accuracy of mimicking human driving behavior is improved, but the safety distance may be reduced
Solution Approach 1:
The system dynamically adjusts the time gap parameter based on real-time detection of surrounding target vehicles. The time gap is not fixed but varies continuously with traffic conditions: it decreases when surrounding vehicles increase (mimicking human drivers who close gaps in congested traffic) and increases when surrounding vehicles decrease. This dynamic adaptation allows the system to maintain both human-like behavior accuracy and appropriate safety distances under varying conditions
Solution Approach 2:
The system implements feedback by continuously monitoring the number of surrounding target vehicles and adjusting the time gap accordingly. The sensor arrangement provides ongoing information about traffic conditions, and the control unit arrangement uses this feedback to modify the ego vehicle speed and maintain an appropriate time gap. This closed-loop feedback mechanism ensures that safety distance is maintained while accurately mimicking human driving responses to traffic conditions
Data Source
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 (vi), 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).