In highly interactive driving scenarios, the actions of one agent greatly influences those of its neighbors. Planning safe motions for autonomous vehicles in such interactive environments, therefore, requires reasoning about the impact of the ego's intended motion plan on nearby agents' behavior. Deep-learning-based models have recently achieved great success in trajectory prediction and many models in the literature allow for ego-conditioned prediction. However, leveraging ego-conditioned prediction remains challenging in downstream planning due to the complex nature of neural networks, limiting the planner structure to simple ones, e.g., sampling-based planner. Despite their ability to generate fine-grained high-quality motion plans, it is difficult for gradient-based planning algorithms, such as model predictive control (MPC), to leverage ego-conditioned prediction due to their iterative nature and need for gradient. We present Interactive Joint Planning (IJP) that bridges MPC with learned prediction models in a computationally scalable manner to provide us the best of both the worlds. In particular, IJP jointly optimizes over the behavior of the ego and the surrounding agents and leverages deep-learned prediction models as prediction priors that the join trajectory optimization tries to stay close to. Furthermore, by leveraging homotopy classes, our joint optimizer searches over diverse motion plans to avoid getting stuck at local minima. Closed-loop simulation result shows that IJP significantly outperforms the baselines that are either without joint optimization or running sampling-based planning.

翻译：在高交互性驾驶场景中，一个智能体的行为会显著影响其邻近智能体的行为。因此，在此类交互环境下为自主车辆规划安全运动，需要考量自车预期运动方案对周围智能体行为的影响。基于深度学习的模型在轨迹预测领域近期取得了巨大成功，文献中诸多模型已支持以自车状态为条件的预测。然而，由于神经网络的复杂性，在底层规划中利用以自车为条件的预测仍具挑战，这导致规划器结构局限于简单形式，例如基于采样的规划器。尽管此类规划器能生成精细的高质量运动方案，但基于梯度的规划算法（如模型预测控制MPC）因自身的迭代特性和对梯度的需求，难以有效利用以自车为条件的预测。我们提出交互式联合规划（IJP），以计算可扩展的方式将MPC与学习型预测模型相衔接，实现两者优势的融合。具体而言，IJP联合优化自车与周围智能体的行为，并利用深度学习预测模型作为预测先验，使联合轨迹优化过程尽量接近该先验。此外，通过利用同伦类，我们的联合优化器可搜索多样化的运动方案，从而避免陷入局部最优。闭环仿真结果表明，IJP显著优于未采用联合优化或仅运行基于采样的规划的基线方法。