To extend the toolbox to support trajectory prediction datasets collected from on-board vehicle sensors, such as the HDD, NuScenes, Argoverse 2, and WOMD datasets, several modifications and additions can be made. Firstly, the preprocessing pipeline would need to be adjusted to accommodate the different data formats and features provided by these datasets. This may involve incorporating additional agent features specific to on-board sensors, such as radar or lidar data, which are commonly available in these datasets.
Secondly, the evaluation metrics in the toolbox would need to be expanded to include performance measures that are relevant to the characteristics of on-board sensor data. For example, metrics that assess the accuracy of sensor-based predictions, such as alignment with sensor readings or detection of occlusions, could be included. Additionally, the toolbox could incorporate specific visualization techniques tailored to the unique data representations of on-board sensor datasets.
Furthermore, the toolbox could provide functionalities for fine-tuning models trained on drone datasets to adapt them to on-board sensor data. This could involve transfer learning techniques that leverage the knowledge gained from drone datasets to improve prediction accuracy on on-board sensor datasets. By incorporating these adjustments and enhancements, the toolbox can effectively support a wider range of trajectory prediction datasets, including those collected from on-board vehicle sensors.