Real-world driving involves intricate interactions among vehicles navigating
through dense traffic scenarios. Recent research focuses on enhancing the
interaction awareness of autonomous vehicles to leverage these interactions in
decision-making. These interaction-aware planners rely on neural-network-based
prediction models to capture inter-vehicle interactions, aiming to integrate
these predictions with traditional control techniques such as Model Predictive
Control. However, this integration of deep learning-based models with
traditional control paradigms often results in computationally demanding
optimization problems, relying on heuristic methods. This study introduces a
principled and efficient method for combining deep learning with constrained
optimization, employing knowledge distillation to train smaller and more
efficient networks, thereby mitigating complexity. We demonstrate that these
refined networks maintain the problem-solving efficacy of larger models while
significantly accelerating optimization. Specifically, in the domain of
interaction-aware trajectory planning for autonomous vehicles, we illustrate
that training a smaller prediction network using knowledge distillation speeds
up optimization without sacrificing accuracy.

结合知识蒸馏的优化，通过训练小型网络，提高了自动驾驶交互感知中的路径规划的速度和精确性。