MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles
This addresses trajectory prediction for autonomous vehicles, but it appears incremental as it combines existing methods like ResNet-50 and temporal probabilistic networks with new data sources.
The paper tackles the problem of predicting ego vehicle trajectories in urban and dense areas by integrating high-definition map images and IMU sensor data, resulting in improved robustness and reliability for autonomous vehicles.
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.