Emergent-scene safety is the key milestone for fully autonomous driving, and reliable on-time prediction is essential to maintain safety in emergency scenarios. However, these emergency scenarios are long-tailed and hard to collect, which restricts the system from getting reliable predictions. In this paper, we build a new dataset, which aims at the long-term prediction with the inconspicuous state variation in history for the emergency event, named the Extro-Spective Prediction (ESP) problem. Based on the proposed dataset, a flexible feature encoder for ESP is introduced to various prediction methods as a seamless plug-in, and its consistent performance improvement underscores its efficacy. Furthermore, a new metric named clamped temporal error (CTE) is proposed to give a more comprehensive evaluation of prediction performance, especially in time-sensitive emergency events of subseconds. Interestingly, as our ESP features can be described in human-readable language naturally, the application of integrating into ChatGPT also shows huge potential. The ESP-dataset and all benchmarks are released at https://dingrui-wang.github.io/ESP-Dataset/.

在自动驾驶中，紧急场景安全是重要的里程碑，而可靠的准时预测对于保持安全至关重要。本文建立了一个新的数据集，旨在长期预测紧急事件的不显著历史状态变化，命名为Extro-Spective Prediction（ESP）问题。基于该数据集，引入了一种适用于ESP的灵活特征编码器作为各种预测方法的无缝插件，并且其一贯的性能提升证明了其有效性。此外，提出了一种名为CTE（clamped temporal error）的新度量标准，可以更全面地评估预测性能，特别是在亚秒级的紧急事件中。有趣的是，由于我们的ESP特征可以自然地用可读的人类语言描述，将其整合到ChatGPT中也展示了巨大的潜力。ESP数据集和所有基准已在此URL发布。

ESP: 突发紧急情景中的长期行为推理的外视预测