A Glimpse into the Future: An Inverse Soft Q-Learning's Soft Actor-Critic Approach for Pedestrian Path Prediction

Anticipating the future trajectories of pedestrians is a essential ability in autonomous vehicles to perform proactive actions and thus reduce dangerous encounters. However, predicting human motion is a task that is inherently challenging due to the influence of social and environmental factors, and the multimodality of future predictions based solely on partial history of their trajectory. The underlying scene and the past trajectory of a pedestrian provide useful indicators for predicting their future steps. Unlike other approaches that utilize supervised learning or generative modeling, Inverse Reinforcement Learning enables a model to learn the pedestrian's reward function that encodes their intentions. This work proposes a framework based on Inverse soft Q-Learning's Soft Actor-Critic Version. The framework utilizes the information about the scene and the past trajectory of a pedestrian together with an attention mechanism to learn the pedestrian's behavior policy. Quantitative and qualitative evaluation on existing pedestrian trajectory prediction benchmarks show comparable results to state-of-the-art baselines.