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。

基于知识蒸馏的自主车辆规划中的可扩展高效交互感知

Towards Scalable & Efficient Interaction-Aware Planning in Autonomous  Vehicles using Knowledge Distillation

Automated driving systems (ADSs) promise a safe, comfortable and efficient
driving experience. However, fatalities involving vehicles equipped with ADSs
are on the rise. The full potential of ADSs cannot be realized unless the
robustness of state-of-the-art improved further. This paper discusses unsolved
problems and surveys the technical aspect of automated driving. Studies
regarding present challenges, high-level system architectures, emerging
methodologies and core functions: localization, mapping, perception, planning,
and human machine interface, were thoroughly reviewed. Furthermore, the
state-of-the-art was implemented on our own platform and various algorithms
were compared in a real-world driving setting. The paper concludes with an
overview of available datasets and tools for ADS development.

本研究综述了自动驾驶系统的现状、面临的挑战和核心功能，并在实际驾驶中实现了各种算法和技术的比较，提出了自动驾驶系统发展所需的数据集和工具建议。