SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments

Abstract

Designing a safe and human-like decision-making system for an autonomous vehicle is a challenging task. Generative imitation learning is one possible approach for automating policy-building by leveraging both real-world and simulated decisions. Previous work that applies generative imitation learning to autonomous driving policies focuses on learning a low-level controller for simple settings. However, to scale to complex settings, many autonomous driving systems combine fixed, safe, optimization-based low-level controllers with highlevel decision-making logic that selects the appropriate task and associated controller. In this paper, we attempt to bridge this gap in complexity by employing Safety-Aware Hierarchical Adversarial Imitation Learning (SHAIL), a method for learning a high-level policy that selects from a set of low-level controller instances in a way that imitates low-level driving data onpolicy. We introduce an urban roundabout simulator that controls non-ego vehicles using real data from the Interaction dataset. We then show empirically that our approach can produce better behavior than previous approaches in driver imitation which have difficulty scaling to complex environments. Our implementation is available at https://github.com/ sisl/InteractionImitation.

Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review

“…In [96], GAIL was applied, but rather than learning low-level controls, the authors proposed a hierarchical model with a safety layer wherein high-level decision-making logic was learned by the model, and then low-level controllers were used to realize the logic. The algorithm, safety-aware hierarchical adversarial imitation learning (SHAIL), in this case, selected from a set of possibilities that targeted a specific velocity at a future time.…”

“…Used high-level human decisions such as lane change as input. Increased the use of hyperparameters so tuning was time consuming [96] Hierarchical model with a safety layer to avoid collisions. Used real-world data to test in simulation.…”