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.

翻译：预测自车轨迹仍是一项关键挑战，尤其在城区和密集区域，这主要源于其他车辆与行人行为的不可预测性。多模态轨迹预测通过融合多种环境数据源来考虑多种可能的未来轨迹，从而提升决策能力。在本方法中，我们利用ResNet-50从高清地图数据中提取图像特征，并采用IMU传感器数据计算速度、加速度与横摆角速度。通过使用时序概率网络计算潜在轨迹，并选取最精确且高概率的轨迹路径。该方法融合高清地图数据，以提升自动驾驶车辆轨迹预测的鲁棒性与可靠性。