Making safe and human-like decisions is an essential capability of autonomous driving systems, and learning-based behavior planning presents a promising pathway toward achieving 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. This framework consists of three components: a behavior generation module that produces a diverse set of candidate behaviors in the form of trajectory proposals, a conditional motion prediction network that predicts future trajectories of other agents based on each proposal, and a scoring module that evaluates the candidate plans using maximum entropy inverse reinforcement learning (IRL). We validate the proposed framework on a large-scale real-world urban driving dataset through comprehensive experiments. The results show that the conditional prediction model can predict distinct and reasonable future trajectories given different trajectory proposals and the IRL-based scoring module can select plans that are close to human driving. The proposed framework outperforms other baseline methods in terms of similarity to human driving trajectories. Additionally, we find that the conditional prediction model improves both prediction and planning performance compared to the non-conditional model. Lastly, we note that learning the scoring module is crucial for aligning the evaluations with human drivers.
翻译:与直接产出决定的现有基于学习的方法不同,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会预测和评价。这个框架由三个部分组成:一个行为生成模块,以轨迹建议的形式产生一套多样的候选行为,一个有条件的运动预测网络,根据每个提案预测其他代理人的未来轨迹,以及一个评分模块,用最大增压反向强化学习(IRL)评估候选人计划。我们通过全面实验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,有条件的预测模型可以预测不同轨迹建议带来的不同和合理的未来轨迹,而基于IRL的评分模块可以选择接近人类驱动的计划。拟议的框架在与人类驱动轨迹相似方面优于其他基线方法。此外,我们发现,有条件的预测模型改进了预测和规划业绩,与不成熟的模型相比,正在调整人类驱动力评估。最后,我们注意到,有条件的预测模型将改进了预测和规划业绩,与关键模型进行升级。</s>