Cooperation-Aware Lane Change Control in Dense Traffic
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Solution Overview
Problem
Current autonomous driving technologies face challenges in implementing effective lane change control in dense traffic scenarios, particularly when interacting with human drivers, as existing methods often rely on probabilistic or scenario-based models that may not adequately account for complex human driving behaviors.
Innovation Solution
A computer-implemented method and system utilizing a controller with an analyzer and a recurrent neural network to predict future states of an ego vehicle and interactive motions of surrounding vehicles, combined with a heuristic algorithm to evaluate and optimize lane change decisions, ensuring cooperation-aware lane change control in dense traffic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If probabilistic models or scenario-based models are used to predict adjacent driver's motions, then the autonomous vehicle can implement lane change control, but the system fails to adequately account for complex human driving behaviors in highly dense traffic areas
Solution Approach 1:
The system changes the modeling parameters from simple probabilistic models to a hybrid approach combining recurrent neural networks with game theory parameters. The neural network learns complex human driving behavior patterns while game theory provides a framework for predicting cooperative and non-cooperative interactions, thereby improving both reliability and adaptability in dense traffic conditions
Solution Approach 2:
The patent combines multiple modeling approaches (recurrent neural networks and game theory) into a composite prediction system. This composite model integrates the pattern recognition capabilities of neural networks with the strategic interaction modeling of game theory, creating a more comprehensive system that handles both individual driver behaviors and collective traffic dynamics in dense environments
2Object-affected harmful factors
If path overlap avoidance techniques are used, then collision prevention is improved, but the system does not ensure smooth and efficient lane changes in dense traffic
Solution Approach 1:
The system implements feedback by continuously monitoring the predicted states of surrounding vehicles and adjusting the lane change trajectory in real-time. The recurrent neural network processes sequential data to predict future positions, and the controller uses this feedback to dynamically modify the lane change path, ensuring both collision avoidance and smooth execution by adapting to actual traffic conditions rather than relying on fixed avoidance paths
3Ease of operation
If existing lane change methods are applied, then basic lane changing capability is achieved, but the system cannot handle interactions with human drivers in dense traffic scenarios
Solution Approach 1:
The patent introduces game theory as an intermediary layer between the basic lane change control system and the complex human driver interactions. This intermediary framework models human drivers as rational agents with different cooperation levels, allowing the system to predict and respond to their behaviors appropriately. The intermediary translates simple lane change commands into context-aware interactions that account for human decision-making patterns in dense traffic
Data Source
AI summary
A system and method for providing cooperation-aware lane change control in dense traffic that include receiving vehicle dynamic data associated with an ego vehicle and receiving environment data associated with a surrounding environment of the ego vehicle. The system and method also include utilizing a controller that includes an analyzer to analyze the vehicle dynamic data and a recurrent neural network to analyze the environment data. The system and method further include executing a heuristic algorithm that sequentially evaluates the future states of the ego vehicle and the predicted interactive motions of the surrounding vehicles to promote the cooperation-aware lane change control in the dense traffic.