<p>In intelligent transportation systems (ITS), pedestrian trajectory prediction is one key task for ensuring the safety and efficient operation of autonomous vehicles (AVs). In pedestrian‒vehicle mixed environments, especially in shared spaces without traffic signals, pedestrians may abruptly alter their path by deciding to cross the road or stop waiting. These abrupt changes in trajectory are not fully reflected in their historical movement patterns, making the prediction of future trajectories solely based on past data challenging. To address this issue, a hierarchical optimization-based (HOB) pedestrian trajectory prediction model is proposed, which is designed to generate more accurate trajectory prediction from the ego-centric perspective by leveraging multisource information. The model consists of two main stages: strategic trajectory prediction and tactical trajectory prediction. In the strategic trajectory prediction phase, historical trajectories and ego vehicle speed are combined to extract features through a multiscale convolutional temporal attention mechanism and generate multimodal strategic trajectories via a conditional variational autoencoder (CVAE). In the tactical trajectory prediction phase, a dynamic window-based aggregation mechanism is employed, which organically integrates strategic features and further fuses scene-level and local-level optical flow information, progressively decoding to generate improved tactical trajectories. Extensive experimental results on the JAAD and PIE datasets reveal that the proposed method outperforms advanced approaches in terms of overall prediction performance.</p>