Cloud Server Vehicle Cooperative Decision-Making
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Solution Overview
Problem
Current vehicle cooperative decision-making methods using image recognition suffer from time delays and inaccuracies, leading to lower road utilization and safety.
Innovation Solution
A method and device for vehicle cooperative decision-making that utilizes a cloud server to receive congestion requests from roadside devices, acquire real-time road scene information, determine congestion scene types, and calculate multi-vehicle oriented decision planning schemes using a preset scene cooperative decision model, which includes road restriction and vehicle state information, to distribute driving operations to vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image recognition is used for vehicle cooperative decision-making, then vehicles can recognize driving intentions and conditions of other vehicles, but it causes time delay and inaccuracy
Solution Approach 1:
The patent replaces the mechanical image recognition system with a communication-based information exchange system. Vehicles directly transmit their state data (position, speed, acceleration, steering angle) to other vehicles and the cloud server, eliminating the need for image capture, processing, and recognition. This substitution of the information acquisition mechanism resolves both the time delay and accuracy issues inherent in image-based approaches.
Solution Approach 2:
The cloud server acts as an intermediary that collects real-time state information from multiple vehicles, processes this data centrally, and generates coordinated decision-making schemes. This intermediary approach enables accurate and timely information sharing without requiring direct image recognition between vehicles, solving the contradiction between recognition speed and accuracy.
2Reliability
If image recognition is used for vehicle cooperative decision-making, then vehicles can identify road conditions, but it results in lower road utilization and safety
Solution Approach 1:
The patent merges the decision-making capabilities of multiple vehicles into a unified cooperative system. By combining real-time state information from all participating vehicles and using centralized cloud processing, the system achieves more reliable and safe decisions while optimizing road utilization through coordinated routing and traffic flow management that image recognition alone cannot provide.
Solution Approach 2:
The system implements continuous feedback loops where vehicles transmit their state information to the cloud server, which processes this data and returns optimized decision-making schemes. This real-time feedback mechanism ensures high reliability and safety by constantly updating decisions based on current road conditions and vehicle states, while also improving road utilization through dynamic route optimization.
3Measurement precision
If centralized cloud server processes multi-vehicle decision-making, then decision accuracy improves, but computing requirements increase
Solution Approach 1:
The patent segments the decision-making process into two parts: data collection and transmission by individual vehicles (lighter computing task), and centralized processing by the cloud server (heavier computing task). This segmentation allows vehicles with limited computing power to participate in cooperative decision-making by only performing data acquisition and communication, while the cloud server handles the complex multi-vehicle coordination algorithms.
Solution Approach 2:
The cloud server serves as an intermediary that offloads the computationally intensive multi-vehicle decision-making calculations from individual vehicles. By centralizing the processing workload, the system achieves high decision accuracy through comprehensive analysis of all vehicle states while individual vehicles maintain low computing requirements, only needing to transmit and receive data.
Data Source
AI summary
The application provides a method, device, electronic device for vehicle cooperative decision-making as well as a computer storage medium. The method for vehicle cooperative decision-making applied at a cloud server includes: receiving a cooperative decision request sent by a roadside device, wherein the cooperative decision request is a request sent by the roadside device after it is recognized that a road is congested based on acquired real-time road information; acquiring information of a road scene included in the cooperative decision request; determining a congestion scene type based on the information of the road scene; calculating multi-vehicle oriented decision planning schemes using a preset scene cooperative decision model based on the congestion scene type; and sending the decision planning schemes to respective vehicles, so that the vehicles perform respective driving operations according to the decision planning schemes.