Egocentric Conformal Prediction for Safe and Efficient Navigation in Dynamic Cluttered Environments

Since safe control of ego-vehicles depends on accurately predicting the future states of surrounding dynamic agents, numerous motion forecasting models [1, 2] have been developed to forecast an agent's future motions from historical data. Nevertheless, these predictions remain inherently prone to error, primarily because they lack information about hidden contexts or intents--such as agents' goals, velocity preferences, or even social relationships among human agents. To address these limitations, conformal prediction (CP) [3, 4] has been employed to reliably assess the models' predictive capabilities. The method offers a principled yet straightforward procedure for calibrating the models. At test time, the calibration results can be used to construct a confidence set that contains the true future states of the environment, assuming that the test and calibration data are exchangeable (i.e., their joint distribution is symmetric). Consequently, CP has been successfully applied to a variety of problems, including reinforcement learning [5, 6], linear This work was supported in part by the Information and Communications Technology Planning and Evaluation (IITP) grants funded by MSIT No. 2022-0-00124, No. 2022-0-00480 and No. RS-2021-II211343, Artificial Intelligence Graduate School Program (Seoul National University). The authors are with the Department of Electrical and Computer Engineering, ASRI, Seoul National University, Seoul 08826, South Korea,{sju5379, jungbbal, insoonyang }@snu.ac.kr arXiv:2504.00447v1