Adaptive Cruise Control Using Target Vehicle Driving Patterns
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Solution Overview
Problem
Existing cooperative adaptive cruise control (CACC) systems require manual adjustment of parameters like inter-vehicle distance and speed level based on road state and target vehicle movement, causing inconvenience.
Innovation Solution
A CACC system that utilizes V2V and V2I communications to collect vehicle and road information, analyzes driving patterns, and automatically sets inter-vehicle distance and speed levels based on target vehicle data, including a control unit to manage driving speed through throttle and brake control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual adjustment of CACC parameters (inter-vehicle distance and speed level) is required based on road state and target vehicle movement, then the system allows flexible adaptation to different conditions, but it causes inconvenience and increases operational complexity for the driver
Solution Approach 1:
The CACC system automatically adjusts inter-vehicle distance and speed level parameters based on road state and target vehicle movement data collected from sensors and communication units. The control unit autonomously processes this information and modifies control parameters without requiring manual driver intervention, allowing the system to serve itself in adapting to changing conditions.
2Ease of operation
If CACC system automatically adjusts parameters based on collected road and vehicle information, then driver convenience is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The CACC system divides the control functionality into separate modules: a communication unit for data collection, an information collection unit for sensor data acquisition, and a control unit for parameter adjustment. This segmentation allows each component to perform its specific function independently, managing overall system complexity through modular architecture while enabling automatic parameter adjustment.
3Measurement precision
If the CACC system collects and processes extensive vehicle and road information using V2V and V2I communications, then the accuracy of automatic control is improved, but the amount of data processing and communication requirements increase
Solution Approach 1:
The system extracts only the essential features and patterns from the collected vehicle and road information that are relevant for controlling the subject vehicle. Rather than processing all raw data, the control unit identifies and utilizes key parameters such as target vehicle speed, acceleration patterns, and road conditions, filtering out unnecessary information to reduce data processing volume while maintaining control accuracy.
Data Source
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AI summary
A cooperative adaptive cruise control (CACC) system acquires a driving pattern of a target vehicle and variably provides an inter-vehicle distance and a responsible speed level of a subject vehicle that are followed by the CACC system based on the driving pattern. The CACC system includes a communication unit receiving vehicle information and road information of a region in which the subject vehicle travels; an information collection unit collecting driving information of a forward vehicle, vehicle information of the subject vehicle, and the road information; and a control unit controlling the inter-vehicle distance and the responsible speed level of the CACC system based on the driving pattern of the target vehicle according to generated control information.