Recently significant progress has been made in vehicle prediction and planning algorithms for autonomous driving. However, it remains quite challenging for an autonomous vehicle to plan its trajectory in complex scenarios when it is difficult to accurately predict its surrounding vehicles' behaviors and trajectories. In this work, to maximize performance while ensuring safety, we propose a novel speculative planning framework based on a prediction-planning interface that quantifies both the behavior-level and trajectory-level uncertainties of surrounding vehicles. Our framework leverages recent prediction algorithms that can provide one or more possible behaviors and trajectories of the surrounding vehicles with probability estimation. It adapts those predictions based on the latest system states and traffic environment, and conducts planning to maximize the expected reward of the ego vehicle by considering the probabilistic predictions of all scenarios and ensure system safety by ruling out actions that may be unsafe in worst case. We demonstrate the effectiveness of our approach in improving system performance and ensuring system safety over other baseline methods, via extensive simulations in SUMO on a challenging multi-lane highway lane-changing case study.

翻译：近年来，自主驾驶的车辆预测与规划算法取得了显著进展。然而，当难以准确预测周围车辆的行为和轨迹时，自动驾驶车辆在复杂场景中规划其轨迹仍极具挑战性。为在保障安全的同时最大化性能，本文提出一种新颖的基于预测-规划接口的推测规划框架，该接口可量化周围车辆的行为级与轨迹级不确定性。该框架利用现有预测算法，能提供周围车辆一种或多种可能行为及轨迹的概率估计，并根据最新系统状态与交通环境自适应调整这些预测。通过考虑所有场景的概率化预测以最大化自车期望收益，并排除最坏情况下可能不安全的动作以保障系统安全，我们实现了规划优化。通过在SUMO环境下对多车道高速公路变道这一挑战性案例进行大量仿真，我们验证了该方法在提升系统性能与保障系统安全性方面相较于其他基线方法的有效性。