  Predicting ego vehicle trajectories remains a critical challenge, especially
in urban and dense areas due to the unpredictable behaviours of other vehicles
and pedestrians. Multimodal trajectory prediction enhances decision-making by
considering multiple possible future trajectories based on diverse sources of
environmental data. In this approach, we leverage ResNet-50 to extract image
features from high-definition map data and use IMU sensor data to calculate
speed, acceleration, and yaw rate. A temporal probabilistic network is employed
to compute potential trajectories, selecting the most accurate and highly
probable trajectory paths. This method integrates HD map data to improve the
robustness and reliability of trajectory predictions for autonomous vehicles.
