ESP: Extro-Spective Prediction for Long-term Behavior Reasoning in Emergency Scenarios
<|reference_start|>ESP: Extro-Spective Prediction for Long-term Behavior Reasoning in Emergency Scenarios: 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/.<|reference_end|>
@article{wang2024esp:,
    title={ESP: Extro-Spective Prediction for Long-term Behavior Reasoning in
  Emergency Scenarios},
    author={Dingrui Wang, Zheyuan Lai, Yuda Li, Yi Wu, Yuexin Ma, Johannes Betz,
  Ruigang Yang, Wei Li},
    journal={arXiv preprint arXiv:2405.04100},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04100},
    primaryClass={cs.CV cs.LG}
}