Online Vehicle Trajectory Prediction using Policy Anticipation Network and Optimization-based Context Reasoning
This work addresses trajectory prediction for autonomous vehicles, offering a novel method to handle multimodal futures and complex contexts, though it is incremental as it builds on existing LSTM and optimization techniques.
The paper tackles vehicle trajectory prediction in urban autonomous driving by combining policy anticipation with optimization-based context reasoning, achieving accurate predictions that adapt to complex traffic configurations as validated in the CARLA simulation platform.
In this paper, we present an online two-level vehicle trajectory prediction framework for urban autonomous driving where there are complex contextual factors, such as lane geometries, road constructions, traffic regulations and moving agents. Our method combines high-level policy anticipation with low-level context reasoning. We leverage a long short-term memory (LSTM) network to anticipate the vehicle's driving policy (e.g., forward, yield, turn left, turn right, etc.) using its sequential history observations. The policy is then used to guide a low-level optimization-based context reasoning process. We show that it is essential to incorporate the prior policy anticipation due to the multimodal nature of the future trajectory. Moreover, contrary to existing regression-based trajectory prediction methods, our optimization-based reasoning process can cope with complex contextual factors. The final output of the two-level reasoning process is a continuous trajectory that automatically adapts to different traffic configurations and accurately predicts future vehicle motions. The performance of the proposed framework is analyzed and validated in an emerging autonomous driving simulation platform (CARLA).