Platooning Trajectory Control Using RL and V2X Feedback
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Solution Overview
Problem
Existing platooning control systems face challenges in stabilizing and efficiently controlling the trajectory of rear vehicles following the lead vehicle during platooning, especially in dynamic and exceptional situations.
Innovation Solution
The proposed solution involves a platooning control device and method that utilize reinforcement learning based on image information and a feedback signal to control a pertinent vehicle to follow the traveling trajectory of a front vehicle. This is achieved through a learning device performing reinforcement learning and a compensation determination unit generating feedback signals by comparing the vehicle's coordinates with a control point on the front vehicle's trajectory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If reinforcement learning is performed using only image information for platooning control, then the system can operate with simpler sensors, but the trajectory following stability and precision deteriorates
Solution Approach 1:
The patent combines image information from cameras with coordinate information from V2X communication to create a hybrid input system for reinforcement learning. This merging of different information sources allows the system to maintain trajectory following precision while avoiding the need for more complex sensor systems like LiDAR or radar.
Solution Approach 2:
The patent introduces coordinate information as an intermediary element that bridges the gap between simple image-based control and precise trajectory following. The coordinate data from V2X communication acts as a mediator that enhances the precision of trajectory tracking without requiring complex sensor hardware.
2Device complexity
If traditional control methods are used for platooning, then the system structure remains simple, but the ability to handle dynamic and exceptional situations deteriorates
Solution Approach 1:
The patent implements dynamic control by using reinforcement learning that continuously adapts to changing conditions during platooning. The control strategy transitions from static traditional methods to dynamic adaptive control, allowing the system to handle exceptional situations while maintaining a relatively simple overall system structure.
Solution Approach 2:
The reinforcement learning system dynamically changes control parameters based on real-time conditions, including image information and coordinate data. This parameter adaptation enables the system to respond to dynamic and exceptional situations without requiring a fundamentally complex control architecture.
3Stability of the object's composition
If reinforcement learning uses comprehensive image information and feedback signals, then trajectory following stability improves, but the computational load and processing time increase
Solution Approach 1:
The patent extracts only the essential features from image information and V2X coordinate data that are relevant for trajectory following. By taking out and processing only the critical information elements rather than all available data, the system maintains trajectory stability while reducing computational processing time.
Solution Approach 2:
The system performs preliminary processing of image and coordinate information to prepare optimized inputs for the reinforcement learning algorithm. This preliminary action reduces the computational burden during real-time decision-making, thereby maintaining stability while minimizing processing time delays.
Data Source
AI summary
A platooning control device includes: a learning device configured to perform reinforcement learning on the basis of image information and a feedback signal and to control a pertinent vehicle so as to follow a traveling trajectory of a front vehicle according to a result of the reinforcement learning; and a compensation determination unit configured to receive a coordinate of a control point regarding the traveling trajectory of the front vehicle from the front vehicle and to compare a coordinate of the pertinent vehicle with the coordinate of the control point, thereby generating the feedback signal.