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：预测其他道路使用者的运动轨迹有助于提升自动驾驶汽车性能。然而，预测时间范围与自动驾驶汽车性能之间的关系仍不明确。尽管存在大量轨迹预测算法，但尚未有研究探讨预测长度变化对自动驾驶安全及其他性能指标的影响，导致预测方法缺乏明确的时域需求定义。本研究通过分析不同预测时域对自动驾驶性能的影响，聚焦安全性、舒适性和效率三个方面填补这一空白。我们采用基于风险的最优预测轨迹规划器开展多项实验，模拟了长达20秒的预测时域。基于仿真结果，提出了根据特定自动驾驶性能指标和应用需求确定最低要求及最优预测时域的框架。研究结果表明：防止与横穿行人碰撞需1.6秒预测时域；7-8秒时域可实现最佳效率；时域延长至15秒可提升乘客舒适性。结论指出预测时域需求具有应用依赖性，并建议针对包含横穿行人的场景，以11.8秒预测时域作为通用参考指标。