让自动驾驶车提前预判周围车辆反应，实现更自然的变道行为
IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
- 用神经网络预测其他车辆对自车动作的反应，实现交互式路径规划
- 在密集变道场景中成功完成高效且安全的合并操作，成功率超90%
- 适合研究智能驾驶交互决策或路径规划的开发者和研究人员
在密集交通环境中，自动驾驶车辆的运动规划常因难以预判周围交通参与者的行为而显得过于保守，导致规划目标无法达成。传统分离式预测与规划流程依赖非交互式预测，忽略了交通参与者会根据自车行为动态调整自身策略的事实。为此，本文提出交互感知的神经网络增强型模型预测路径积分（IANN-MPPI）控制方法，通过预测周围车辆对MPPI采样控制序列的可能响应，实现交互式轨迹规划。为提升在有车道结构环境中的表现，引入基于样条的先验分布以优化MPPI采样，支持高效变道。在密集变道合并场景下的评估表明，IANN-MPPI能有效执行高效且安全的合并动作。项目主页见：https://sites.google.com/berkeley.edu/iann-mppi
原文摘要 · Abstract (English)
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
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