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As the motion of pedestrians is largely unpredictable, situational awareness presents a challenge for safe autonomous driving in urban areas. In particular, conventional sensor information about the dynamic states involved in determining and predicting pedestrian motion, including the walking speed, is significantly affected by latency when pedestrians suddenly increase their pace. In this paper, we propose a framework for predicting the steady-state walking speed of sudden pedestrian movement at the early stage of walking after heel-off. Based on the analysis that some motion cues during gait initiation are related to the steady-state walking speed, a fuzzy inference framework for predicting the steady-state walking speed, where the related motion cues are input to the inference model, is developed. The proposed framework can accurately predict the steady-state walking speed, even at the end of the first gait cycle. Moreover, the future trajectory of the pedestrian can be predicted using the piecewise linear speed model. Using the proposed framework, installed on the edge server of the cooperative-intelligent transportation system (C-ITS), this study aims to ensure the safety of autonomous vehicles by enabling them to successfully navigate the danger caused by sudden pedestrian movement. Experimental results obtained from testing the system at a real urban intersection verify the value offered by the proposed framework. INDEX TERMSAutonomous vehicle, C-ITS, Fuzzy inference, Pedestrian, Sudden pedestrian, Walking speed prediction I. INTRODUCTION 1 Autonomous vehicles must inevitably navigate traffic in-2 volving non-autonomous vehicles and vulnerable road users, 3 including pedestrians, whose unpredictable actions pose the 4 biggest challenge to safe vehicle autonomy. Urban areas such 5 as intersections are particularly problematic. In such areas, 6 the line of sight of autonomous vehicle sensors is often 7 blocked or interrupted by a variety of obstacles. Moreover, 8 the behavior of pedestrians is much more unpredictable than 9 that of massive objects such as vehicles. Consequently, it is 10 very difficult for autonomous vehicles to achieve appropriate 11 situational awareness in terms of possible threats to pedes-12 trians. The cooperative-intelligent transportation system (C-13 ITS) is a promising solution for supporting autonomous driv-14 ing, as contemporary C-ITS technology incorporates multi-

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Trajectory prediction is gaining attention as a form of situational awareness because it is an essential component of the support system of autonomous driving, particularly in urban areas. A promising application is cooperative driving automation, where the traffic scene is monitored by roadside sensors with undisrupted views. A critical problem is that these sensors are adversely affected by inclement weather, including drenching rain or large amounts of snow, in which case the reliability of the prediction results can be significantly compromised. To address these problems, this study proposes a framework for robust vehicle-trajectory predictions based on the Chebyshev transform. In the proposed framework, the original trajectory snippets (partial trajectories) are Chebyshev-transformed, and the resulting coefficients form new snippets. The LSTM (long-short term memory) encoder-decoder structure was trained and tested using these new coefficient snippets, which were extracted from a public vehicle trajectory dataset. The performance and robustness of the proposed framework were verified by emulating sensor data that were incomplete as a result of environmental factors. The proposed framework provides stable and accurate longterm trajectory prediction because the Chebyshev transform is robust to incomplete sensor data by virtue of its uniform nature.