With the release of open source datasets such as nuPlan and Argoverse, the research around learning-based planners has spread a lot in the last years. Existing systems have shown excellent capabilities in imitating the human driver behaviour, but they struggle to guarantee safe closed-loop driving. Conversely, optimization-based planners offer greater security in short-term planning scenarios. To confront this challenge, in this paper we propose a novel hybrid motion planner that integrates both learning-based and optimization-based techniques. Initially, a multilayer perceptron (MLP) generates a human-like trajectory, which is then refined by an optimization-based component. This component not only minimizes tracking errors but also computes a trajectory that is both kinematically feasible and collision-free with obstacles and road boundaries. Our model effectively balances safety and human-likeness, mitigating the trade-off inherent in these objectives. We validate our approach through simulation experiments and further demonstrate its efficacy by deploying it in real-world self-driving vehicles.

翻译：随着nuPlan和Argoverse等开源数据集的发布，基于学习的规划器研究在过去几年中得到了广泛开展。现有系统在模仿人类驾驶员行为方面表现出卓越能力，但在确保安全闭环驾驶方面仍存在困难。相反，基于优化的规划器在短期规划场景中能提供更高的安全性。为应对这一挑战，本文提出一种新型混合运动规划器，融合了基于学习与基于优化的技术。首先，多层感知机（MLP）生成类人轨迹，随后通过基于优化的组件进行精细化处理。该组件不仅最小化跟踪误差，还能计算出运动学可行且与障碍物、道路边界无碰撞的轨迹。我们的模型有效平衡了安全性与类人性，缓解了这两个目标之间的固有权衡。我们通过仿真实验验证了所提方法，并进一步通过在实际自动驾驶车辆中的部署证明了其有效性。