Context-Aware Vehicle Navigation for Emergency Message Priority
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Solution Overview
Problem
Current vehicle control systems lack effective methods to enhance safety in dynamic driving environments, particularly in situations where emergency situations arise due to obstacles or collisions, as they fail to prioritize and rapidly respond to emergency context messages within vehicle networks.
Innovation Solution
A context-aware navigation protocol (CNP) is introduced that enables vehicles to receive and prioritize emergency context messages (ECM) over cooperation context messages (CCM), allowing for real-time analysis of mobility information to detect obstacles, calculate collision probabilities, and adjust vehicle paths to avoid collisions, using a cluster head to control member vehicles and minimize collision impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicles transmit both cooperation context messages (CCM) and emergency context messages (ECM) in vehicle networks, then comprehensive mobility information is available, but response time to emergency situations increases due to message prioritization delays
Solution Approach 1:
The patent segments mobility information transmission into two distinct message types: Cooperation Context Messages (CCM) for normal driving coordination and Emergency Context Messages (ECM) for urgent situations. This segmentation allows the system to handle different message types with appropriate priority levels, ensuring ECMs are processed immediately while CCMs maintain regular transmission schedules, thus resolving the contradiction between comprehensive information availability and rapid emergency response.
Solution Approach 2:
The patent implements preliminary action by pre-defining priority levels and transmission protocols for different message types before emergencies occur. The system预先 establishes that ECMs have higher priority than CCMs, so when an emergency situation arises, the prioritization mechanism is already in place and can immediately activate without delay, ensuring rapid response while maintaining normal communication flow.
2Measurement precision
If vehicles continuously monitor and analyze mobility information from all surrounding vehicles, then collision detection accuracy improves, but computational load and processing time increase
Solution Approach 1:
The patent applies local quality by differentiating the processing intensity based on message type and situational context. For ECMs, the system performs intensive real-time analysis with high priority to detect obstacles and calculate collision probabilities accurately. For CCMs, the system uses lighter processing for routine coordination. This localized adjustment of processing quality optimizes both detection accuracy and computational efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors the results of mobility information analysis and adjusts processing priorities accordingly. When potential collisions are detected through ECM analysis, the system intensifies monitoring and processing for affected vehicle clusters, providing feedback-driven adaptive processing that improves detection accuracy without requiring maximum computational load at all times.
3Reliability
If the system calculates collision probability and maneuvers member vehicles to avoid collisions, then collision avoidance capability improves, but control message transmission time and coordination complexity increase
Solution Approach 1:
The patent uses preliminary action by pre-establishing cluster structures with designated cluster heads and predefined maneuvering protocols before emergencies occur. When ECMs are received, the system can immediately activate pre-planned collision avoidance maneuvers without needing to negotiate control authority or establish coordination protocols in real-time, significantly reducing coordination time while maintaining effective collision avoidance.
Solution Approach 2:
The patent introduces cluster heads as intermediary controllers that manage collision avoidance coordination for groups of vehicles. Instead of each vehicle independently calculating and executing maneuvers, the cluster head receives ECMs, calculates collision probabilities, and coordinates maneuvers for all member vehicles. This intermediary approach centralizes the complex calculations and reduces overall coordination time by eliminating redundant computations across multiple vehicles.
Data Source
AI summary
Provided is a method for performing communication with a first vehicle in a vehicle network, including: receiving, from a second vehicle, mobility information of the second vehicle, the mobility information including 1) a cooperation context message (CCM) or 2) an emergency context message (ECM); and controlling traveling based on the mobility information, in which the CCM may include motion information of the second vehicle, and the ECM may include information notifying an emergency situation related to the second vehicle.