Multi-Task Invariant Representation Imitation Learning for Autonomous Driving

Abstract

Imitation learning is a promising approach to acquiring autonomous driving policies by mimicking human driver behaviors. However, a major drawback of existing driving policies derived from imitation learning is their proneness to capturing spurious correlations, owing to the lack of an explicit causal model. Deploying such policies in unpredictable real-world environments poses severe risks, as spurious correlations may result in flawed decisions that compromise safety. To tackle this challenge, we introduce a novel approach called Multi-Task Invariant Representation Imitation Learning (MIRIL). MIRIL combines invariant learning with imitation learning to identify cross-environment invariant causal representations from driving demonstrations in various scenarios. These representations are then fed into multiple downstream branches for multi-task learning, including policy learning, perception prediction, invariant representation learning, and transition dynamics learning. Through the multi-task learning approach, the model not only makes consistent driving decisions across different environments but also perceives the vehicle's surroundings, thereby improving adaptability and robustness in diverse driving conditions. This enables MIRIL to effectively handle a wide range of driving scenarios, ensuring safety and efficiency. Supported by clear metrics, this paper details our comprehensive experimental setup, including datasets, benchmarks, and comparative analyses, underscoring the capability of MIRIL to significantly boost system generalization and excel in decision-making significantly.