Learning-based approaches have achieved impressive performance for autonomous driving and an increasing number of data-driven works are being studied in the decision-making and planning module. However, the reliability and the stability of the neural network is still full of challenges. In this paper, we introduce a hierarchical imitation method including a high-level grid-based behavior planner and a low-level trajectory planner, which is not only an individual data-driven driving policy and can also be easily embedded into the rule-based architecture. We evaluate our method both in closed-loop simulation and real world driving, and demonstrate the neural network planner has outstanding performance in complex urban autonomous driving scenarios.
翻译:学习驱动的自动驾驶在决策和规划模块上取得了令人瞩目的成果。然而,神经网络的可靠性和稳定性仍然存在挑战。本文介绍了一种分层模仿学习方法,包括高层面基于网格的行为规划和低层面的轨迹规划,该方法不仅是一种针对个体数据驱动的驾驶策略,而且也可以轻松嵌入基于规则的体系结构中。我们在闭环仿真和真实世界驾驶中评估了我们的方法,并证明了神经网络规划者在复杂的城市自动驾驶场景中具有卓越性能。