The content discusses the integration of neural-network-based prediction models with traditional optimization techniques, such as Model Predictive Control (MPC), for interaction-aware trajectory planning in autonomous vehicles. This integration leads to computationally complex neural-network-based optimization problems, which pose challenges for real-time implementation.
To address this challenge, the authors propose employing knowledge distillation to train a smaller and more efficient "student" prediction network from a larger "teacher" network. The student network is designed to take the ego vehicle's candidate trajectory as input and directly produce the interactive predictions, avoiding the iterative process required by the teacher network.
Experiments show that the student network can achieve a significant fivefold improvement in computation time compared to the teacher network, without any significant loss in accuracy. This acceleration is crucial for enabling the real-time implementation of the interaction-aware trajectory planner. The authors also discuss the training process for both the teacher and student networks, including the use of regression-based and adversarial training approaches.
The findings of this work have broad applications in domains involving closed-loop control systems with neural networks, leading to neural-network-based optimization problems, such as robot arm control and aircraft stability enhancement.
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