Predicting the movement of other road users is beneficial for improving automated vehicle (AV) performance. However, the relationship between the time horizon associated with these predictions and AV performance remains unclear. Despite the existence of numerous trajectory prediction algorithms, no studies have been conducted on how varying prediction lengths affect AV safety and other vehicle performance metrics, resulting in undefined horizon requirements for prediction methods. Our study addresses this gap by examining the effects of different prediction horizons on AV performance, focusing on safety, comfort, and efficiency. Through multiple experiments using a state-of-the-art, risk-based predictive trajectory planner, we simulated predictions with horizons up to 20 seconds. Based on our simulations, we propose a framework for specifying the minimum required and optimal prediction horizons based on specific AV performance criteria and application needs. Our results indicate that a horizon of 1.6 seconds is required to prevent collisions with crossing pedestrians, horizons of 7-8 seconds yield the best efficiency, and horizons up to 15 seconds improve passenger comfort. We conclude that prediction horizon requirements are application-dependent, and recommend aiming for a prediction horizon of 11.8 seconds as a general guideline for applications involving crossing pedestrians.

翻译：预测其他道路参与者的运动有助于提升自动驾驶汽车（AV）的性能。然而，预测时间跨度与自动驾驶汽车性能之间的关系仍不明确。尽管已有大量轨迹预测算法，但尚未有研究探讨不同预测长度如何影响自动驾驶汽车的安全性及其他车辆性能指标，导致预测方法的时域需求尚未明确界定。本研究通过考察不同预测时域对自动驾驶汽车性能的影响，并重点关注安全性、舒适性与效率，填补了这一空白。通过采用最先进的基于风险的预测轨迹规划器开展多项实验，我们模拟了长达20秒的预测时域。基于仿真结果，我们提出一个框架，用于根据具体的自动驾驶汽车性能指标和应用需求来规定最小必要及最优预测时域。结果表明：避免与横穿行人发生碰撞需要1.6秒的预测时域；7-8秒的预测时域可实现最佳效率；而长达15秒的预测时域可提升乘客舒适性。我们得出的结论是，预测时域需求具有应用依赖性，并建议以11.8秒的预测时域作为涉及横穿行人场景的通用指导标准。