标题： IANN-MPPI：用于自动驾驶的交互感知神经网络增强模型预测路径积分方法 显示英文标题

标题： IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving

摘要： 自动驾驶车辆（AVs）在密集交通中的运动规划具有挑战性，通常会导致过于保守的行为和未达成的规划目标。 这一挑战源于自动驾驶车辆难以预测和应对周围代理的交互行为。 传统的解耦预测和规划流程依赖于非交互预测，忽视了代理通常会根据自动驾驶车辆的行为调整自身行为的事实。 为了解决这个问题，我们提出了交互感知神经网络增强模型预测路径积分（IANN-MPPI）控制，通过预测周围代理如何对MPPI采样的每个控制序列做出反应，从而实现交互式轨迹规划。 为了提高在结构化车道环境中的性能，我们引入了基于样条的MPPI采样分布先验，实现了高效的变道行为。 我们在密集交通合并场景中评估了IANN-MPPI，证明了其执行高效合并操作的能力。 我们的项目网站位于 https://sites.google.com/berkeley.edu/iann-mppi 显示英文摘要

摘要： Motion planning for autonomous vehicles (AVs) in dense traffic is challenging, often leading to overly conservative behavior and unmet planning objectives. This challenge stems from the AVs' limited ability to anticipate and respond to the interactive behavior of surrounding agents. Traditional decoupled prediction and planning pipelines rely on non-interactive predictions that overlook the fact that agents often adapt their behavior in response to the AV's actions. To address this, we propose Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral (IANN-MPPI) control, which enables interactive trajectory planning by predicting how surrounding agents may react to each control sequence sampled by MPPI. To improve performance in structured lane environments, we introduce a spline-based prior for the MPPI sampling distribution, enabling efficient lane-changing behavior. We evaluate IANN-MPPI in a dense traffic merging scenario, demonstrating its ability to perform efficient merging maneuvers. Our project website is available at https://sites.google.com/berkeley.edu/iann-mppi