Making safe and human-like decisions is an essential capability of autonomous driving systems and learning-based behavior planning is a promising pathway toward this objective. Distinguished from existing learning-based methods that directly output decisions, this work introduces a predictive behavior planning framework that learns to predict and evaluate from human driving data. Concretely, a behavior generation module first produces a diverse set of candidate behaviors in the form of trajectory proposals. Then the proposed conditional motion prediction network is employed to forecast other agents' future trajectories conditioned on each trajectory proposal. Given the candidate plans and associated prediction results, we learn a scoring module to evaluate the plans using maximum entropy inverse reinforcement learning (IRL). We conduct comprehensive experiments to validate the proposed framework on a large-scale real-world urban driving dataset. The results reveal that the conditional prediction model is able to forecast multiple possible future trajectories given a candidate behavior and the prediction results are reactive to different plans. Moreover, the IRL-based scoring module can properly evaluate the trajectory proposals and select close-to-human ones. The proposed framework outperforms other baseline methods in terms of similarity to human driving trajectories. Moreover, we find that the conditional prediction model can improve both prediction and planning performance compared to the non-conditional model, and learning the scoring module is critical to correctly evaluating the candidate plans to align with human drivers.
翻译:与直接产出决定的现有基于学习的方法相区别,这项工作引入了一个预测行为规划框架,从人类驱动数据中学习预测和评价。具体地说,行为生成模块首先以轨迹建议的形式产生一套不同的候选行为。然后,拟议的有条件运动预测网络用于预测其他代理人以每个轨迹建议为条件的未来轨迹。根据候选人计划和相关预测结果,我们学习了一个评分模块,以使用最大限度的反向强化学习(IRL)来评价计划。我们进行全面实验,以验证关于大规模真实世界城市驱动数据集的拟议框架。结果显示,有条件预测模型能够预测多种可能的未来轨迹,因为候选人的行为和预测结果对不同的计划都有反应。此外,基于IRL的评分模块可以正确评价轨迹建议,并选择接近人类的预测结果。拟议的框架在类似性模拟强化学习方面优于其他基线方法,比照人类驱动性预测的模型,比照模型进行不正确的学习。