# DRIVE: Dynamic Rule Inference and Verified Evaluation for Constraint-Aware Autonomous Driving

arXiv: https://arxiv.org/abs/2508.04066 · HTML: https://mlsanity.com/paper/2508.04066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 14 of 14)

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- [Learning Deformable Object Manipulation from Expert Demonstrations](https://mlsanity.com/paper/2207.10148/) (2022)
- [Benchmarking Constraint Inference in Inverse Reinforcement Learning](https://mlsanity.com/paper/2206.09670/) (2022)
- [Learning Soft Constraints From Constrained Expert Demonstrations](https://mlsanity.com/paper/2206.01311/) (2022)
- [Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms](https://mlsanity.com/paper/2012.00889/) (2020)
- [A Computationally Efficient Method for Learning Exponential Family Distributions](https://mlsanity.com/paper/2110.15397/) (2021)
- [A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges](https://mlsanity.com/paper/2409.07569/) (2024)
- [Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey](https://mlsanity.com/paper/2404.16356/) (2024)
- [Confidence Aware Inverse Constrained Reinforcement Learning](https://mlsanity.com/paper/2406.16782/) (2024)
- [A Reinforcement Learning Environment for Polyhedral Optimizations](https://mlsanity.com/paper/2104.13732/) (2021)
- [Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models](https://mlsanity.com/paper/2403.02431/) (2024)
- [Learning-Augmented Model-Based Multi-Robot Planning for Time-Critical Search and Inspection Under Uncertainty](https://mlsanity.com/paper/2507.06129/) (2025)
- [Exponential Family Matrix Completion under Structural Constraints](https://mlsanity.com/paper/1509.04397/) (2015)

## Abstract (arXiv)

Understanding and adhering to soft constraints is essential for safe and socially compliant autonomous driving. However, such constraints are often implicit, context-dependent, and difficult to specify explicitly. In this work, we present DRIVE, a novel framework for Dynamic Rule Inference and Verified Evaluation that models and evaluates human-like driving constraints from expert demonstrations. DRIVE leverages exponential-family likelihood modeling to estimate the feasibility of state transitions, constructing a probabilistic representation of soft behavioral rules that vary across driving contexts. These learned rule distributions are then embedded into a convex optimization-based planning module, enabling the generation of trajectories that are not only dynamically feasible but also compliant with inferred human preferences. Unlike prior approaches that rely on fixed constraint forms or purely reward-based modeling, DRIVE offers a unified framework that tightly couples rule inference with trajectory-level decision-making. It supports both data-driven constraint generalization and principled feasibility verification. We validate DRIVE on large-scale naturalistic driving datasets, including inD, highD, and RoundD, and benchmark it against representative inverse constraint learning and planning baselines. Experimental results show that DRIVE achieves 0.0% soft constraint violation rates, smoother trajectories, and stronger generalization across diverse driving scenarios. Verified evaluations further demonstrate the efficiency, explanability, and robustness of the framework for real-world deployment.