# Reactive and Safe Road User Simulations using Neural Barrier Certificates

arXiv: https://arxiv.org/abs/2109.06689 · HTML: https://mlsanity.com/paper/2109.06689/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 19 of 19)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Safe Model-based Reinforcement Learning with Stability Guarantees](https://mlsanity.com/paper/1705.08551/) (2017)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates](https://mlsanity.com/paper/2101.05436/) (2021)
- [Multi-Agent Imitation Learning for Driving Simulation](https://mlsanity.com/paper/1803.01044/) (2018)
- [Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approach](https://mlsanity.com/paper/2003.04950/) (2020)
- [Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus](https://mlsanity.com/paper/1902.00487/) (2019)
- [Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning](https://mlsanity.com/paper/1903.05766/) (2019)
- [Robust Regression for Safe Exploration in Control](https://mlsanity.com/paper/1906.05819/) (2019)
- [Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions](https://mlsanity.com/paper/1903.09885/) (2019)
- [Learning Barrier Functions with Memory for Robust Safe Navigation](https://mlsanity.com/paper/2011.01899/) (2020)
- [Permissive Barrier Certificates for Safe Stabilization Using Sum-of-squares](https://mlsanity.com/paper/1802.08917/) (2018)
- [Online Parameter Estimation for Human Driver Behavior Prediction](https://mlsanity.com/paper/2005.02597/) (2020)
- [AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints](https://mlsanity.com/paper/1703.08561/) (2017)

## Built on by (most cited first, 7 of 7)

- [Safe Control Under Input Limits with Neural Control Barrier Functions](https://mlsanity.com/paper/2211.11056/) (2022)
- [Diverse Controllable Diffusion Policy with Signal Temporal Logic](https://mlsanity.com/paper/2503.02924/) (2025)
- [Unifying Qualitative and Quantitative Safety Verification of DNN-Controlled Systems](https://mlsanity.com/paper/2404.01769/) (2024)
- [Logically Constrained Robotics Transformers for Enhanced Perception-Action Planning](https://mlsanity.com/paper/2408.05336/) (2024)
- [ConBaT: Control Barrier Transformer for Safe Policy Learning](https://mlsanity.com/paper/2303.04212/) (2023)
- [Hybrid Systems Neural Control with Region-of-Attraction Planner](https://mlsanity.com/paper/2303.10327/) (2023)
- [Geometry of Radial Basis Neural Networks for Safety Biased Approximation of Unsafe Regions](https://mlsanity.com/paper/2210.05596/) (2022)

## Abstract (arXiv)

Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications. In this work, we proposed a reactive agent model which can ensure safety without comprising the original purposes, by learning only high-level decisions from expert data and a low-level decentralized controller guided by the jointly learned decentralized barrier certificates. Empirical results show that our learned road user simulation models can achieve a significant improvement in safety comparing to state-of-the-art imitation learning and pure control-based methods, while being similar to human agents by having smaller errors to the expert data. Moreover, our learned reactive agents are shown to generalize better to unseen traffic conditions, and react better to other road users and therefore can help understand challenging planning problems pragmatically.