Interaction-Aware AV Trajectory Planning With Neural MPC
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Solution Overview
Problem
Current motion planning for autonomous vehicles in shared driving spaces is inadequate, as they rely on simplistic predictive models that fail to accurately predict interactions with other vehicles, especially in complex scenarios, leading to inefficiencies and potential collisions.
Innovation Solution
The integration of a neural network, such as a social generative adversarial network or graph-based spatial-temporal convolutional network, into model predictive control (MPC) optimization to determine interaction-aware trajectories, using alternating direction method of multipliers (ADMM) and Broyden-Fletcher-Goldfarb-Shanno sequential quadratic programming (BFGS-SQP) to ensure locally optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple predictive models (constant velocity, bounded disturbances, known trajectories) are used to predict other vehicles' trajectories, then the computational complexity is low and the system is easy to operate, but the prediction accuracy is insufficient especially in dense traffic scenarios requiring interaction
Solution Approach 1:
The patent replaces traditional mechanical/mathematical predictive models (constant velocity, bounded disturbances) with a neural network-based data-driven model. The neural network learns complex interaction patterns from historical trajectory data, substituting simple physics-based assumptions with adaptive pattern recognition that accurately captures vehicle behavior in dense traffic scenarios.
Solution Approach 2:
The patent transforms the predictive modeling approach by changing from fixed-parameter models (constant velocity) to dynamic parameter models where the neural network learns optimal parameters from data. The model adapts its parameters based on observed traffic patterns, enabling accurate prediction of interactive behaviors in dense traffic while maintaining computational efficiency through learned representations.
2Reliability
If neural network integration into MPC optimization is implemented, then the trajectory prediction accuracy and interaction awareness are improved, but the computational complexity and optimization difficulty increase
Solution Approach 1:
The patent performs preliminary training of the neural network offline using historical trajectory data, so that when the MPC optimization runs in real-time, the network is already trained and ready to provide accurate predictions. This preliminary action separates the computationally intensive training phase from the real-time optimization phase, reducing the burden on the MPC solver while maintaining high prediction accuracy.
Solution Approach 2:
The patent introduces the neural network as an intermediary component between the MPC optimizer and the trajectory prediction task. The neural network acts as a mediator that processes raw observational data and outputs predicted trajectories, which then feed into the MPC optimization. This intermediary structure allows the MPC solver to work with pre-processed, high-quality predictions without directly handling the complexity of raw data analysis.
3Manufacturing precision
If sophisticated optimization methods (ADMM, BFGS-SQP) are used to solve MPC with neural network integration, then the solution optimality is improved, but the computational time and algorithmic complexity increase
Solution Approach 1:
The patent applies sophisticated optimization methods like ADMM and BFGS-SQP only when necessary to achieve high-precision solutions, rather than using them for all trajectory planning scenarios. In less critical situations, simpler optimization approaches may suffice, allowing the system to balance computational time against solution precision dynamically based on the specific operational context and safety requirements.
Data Source
AI summary
According to one aspect, a system for interaction-aware trajectory planning may include a memory storing one or more instructions and a processor executing one or more of the instructions stored on the memory to perform one or more steps, one or more acts, or one or more actions. For example, the processor may perform determining an interaction-aware trajectory for an autonomous vehicle (AV) traveling in an operating environment including one or more other vehicles using model predictive control (MPC) optimization. The MPC optimization may integrate a neural network which receives one or more observations of the AV and one or more observations of one or more of the other vehicles and outputs predicted trajectories for the AV and one or more of the other vehicles a time step into the future. The processor may perform implementing the interaction-aware trajectory for the AV.