Deep learning-based interaction-aware trajectory prediction for autonomous vehicles - Mo, Xiaoyu (Nanyang Technological University, 2022)
Predicting future trajectories of surrounding agents and conducting motion planning based on interaction predictions are of great importance for ensuring the safety and efficiency of autonomous driving in real-world scenarios, especially under critical driving scenarios. However, trajectory prediction is challenging due to its highly interactive and dynamic attributes. The future trajectory of an agent is usually affected by multiple factors, including the agent's dynamics, its interaction with other surrounding agents, and the road structure. The motion patterns of different traffic participants, such as vehicles and pedestrians, are different and need to be considered separately. Therefore, in real-world applications, trajectory predictors should be able to simultaneously predict the motions of heterogeneous traffic participants. Besides, as for a target agent, there are a variable number of possible future trajectories existing, and this inherent multimodality characteristic should also be considered. Further, as trajectory prediction is not the end goal, it should be incorporated into downstream motion planning and control modules to further improve the overall performance of autonomous vehicles. This integration is also challenging and worthwhile investigating.