1. [Publications](/publications) 2. BITS: Bi-level Imitation for Traffic Simulation # BITS: Bi-level Imitation for Traffic Simulation ![](/sites/default/files/styles/wide/public/publications/featured_8.png?itok=kcBITvIT) Simulation is the key to scaling up validation and verification for robotic systems such as autonomous vehicles. Despite advances in high-fidelity physics and sensor simulation, a critical gap remains in simulating realistic behaviors of road users. This is because, unlike simulating physics and graphics, devising first principle models for human-like behaviors is generally infeasible. In this work, we take a data-driven approach and propose a method that can learn to generate traffic behaviors from real-world driving logs. The method achieves high sample efficiency and behavior diversity by exploiting the bi-level hierarchy of driving behaviors by decoupling the traffic simulation problem into high-level intent inference and low-level driving behavior imitation. The method also incorporates a planning module to obtain stable long-horizon behaviors. We empirically validate our method, named Bi-level Imitation for Traffic Simulation (BITS), with scenarios from two large-scale driving datasets and show that BITS achieves balanced traffic simulation performance in realism, diversity, and long-horizon stability. We also explore ways to evaluate behavior realism and introduce a suite of evaluation metrics for traffic simulation. Finally, as part of our core contributions, we develop and open source a software tool that unifies data formats across different driving datasets and converts scenes from existing datasets into interactive simulation environments. ## Authors [Danfei Xu](/person/danfei-xu) [Yuxiao Chen](/person/yuxiao-chen) [Boris Ivanovic](/person/boris-ivanovic) [Marco Pavone](/person/marco-pavone) ## Publication Date Monday, May 29, 2023 ## Published in [IEEE International Conference on Robotics and Automation (ICRA) 2023](https://www.icra2023.org/) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Autonomous Vehicles](/research-area/autonomous-vehicles) [Generative AI](/research-area/generative-ai) ## External Links [Paper](https://arxiv.org/abs/2208.12403) ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .