Cooperative Driving Maneuver Execution via V2V Communication
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Solution Overview
Problem
Existing cooperative driving systems face challenges in identifying and coordinating with relevant vehicles to execute maneuvers safely and effectively, especially in complex traffic situations where not all vehicles can participate in joint maneuvers due to their location or lane usage.
Innovation Solution
The method employs vehicle-to-vehicle communication to identify and adapt to cooperation vehicles by determining maneuvering areas and predicting driving behaviors, allowing vehicles to autonomously or semi-autonomously execute cooperative driving maneuvers by filtering relevant vehicles and adjusting trajectories based on environmental perception messages and digital roadmaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If vehicle-to-vehicle communication is used to identify cooperation vehicles, then the ability to execute cooperative driving maneuvers is improved, but the complexity of detecting and measuring relevant vehicles increases
Solution Approach 1:
The system segments the traffic environment by dividing vehicles into different categories: cooperation vehicles (those willing and able to participate in cooperative maneuvers), maneuvering vehicles (those initiating maneuvers), and other vehicles. This segmentation is achieved through evaluating specific criteria such as vehicle position relative to maneuvering areas, lane usage patterns, and communication capabilities. By segmenting the vehicle population, the system reduces the complexity of identifying relevant cooperation partners among all surrounding vehicles.
Solution Approach 2:
The system performs preliminary evaluation of vehicles to determine their suitability for cooperation before actual cooperative maneuvers are executed. This includes pre-identifying potential cooperation vehicles by assessing their current state, predicting their future behavior, and determining their willingness to cooperate. This preliminary action filters out unsuitable vehicles early in the process, reducing the measurement and detection complexity during actual maneuver execution.
2Reliability
If the system filters and adapts to relevant cooperation vehicles, then the safety of driving maneuvers is improved, but the computational requirements and system complexity increase
Solution Approach 1:
The system dynamically adapts its behavior based on the identified cooperation vehicles. Rather than using fixed rules, the maneuvering vehicle continuously adjusts its driving behavior to match the presumable driving behavior of cooperation vehicles. This dynamic adaptation includes adjusting acceleration profiles, speed limits, and trajectory parameters in real-time based on the cooperative vehicles' responses and environmental conditions. This dynamic approach improves safety by ensuring coordinated action while managing complexity through adaptive rather than purely predetermined control strategies.
Solution Approach 2:
The system implements feedback mechanisms where the maneuvering vehicle monitors the actual behavior of cooperation vehicles during maneuvers and adjusts its own behavior accordingly. Communication channels provide continuous feedback about the state and intentions of cooperation vehicles, allowing the system to verify whether cooperative behavior is being maintained and to make real-time adjustments. This feedback loop enhances safety by ensuring all vehicles remain coordinated while managing complexity through iterative adjustment rather than requiring perfect initial planning.
3Productivity
If real-time communication and adaptation are implemented, then the effectiveness of cooperative maneuvers is improved, but the energy consumption increases
Solution Approach 1:
The system implements periodic communication and evaluation cycles rather than continuous operation. Vehicles communicate at specific intervals and re-evaluate cooperation status at defined checkpoints during maneuvers. This periodic action maintains the effectiveness of cooperative maneuvers by ensuring regular coordination while significantly reducing energy consumption compared to continuous communication and processing. The system balances the need for real-time coordination with energy efficiency by optimizing the frequency of communication and evaluation based on maneuver phase and environmental conditions.
Data Source
AI summary
A method for autonomously or semi-autonomously carrying out a cooperative driving maneuver and a vehicle. Provision is made for a maneuvering vehicle which plans the execution of a driving maneuver to determine a maneuvering area of a road in which the driving maneuver is potentially executed, to communicate with one or more vehicles via vehicle-to-vehicle communication to detect one or more cooperation vehicles which will presumably be inside the maneuvering area during the execution of the driving maneuver, and to adapt its own driving behavior to the presumable driving behavior of the one or more cooperation vehicles to execute the planned driving maneuver. The disclosure provides a possibility which, by vehicle-to-vehicle communication, allows vehicles for jointly carrying out a cooperative driving maneuver to be identified and then allows the cooperative driving maneuver to be executed.