# The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior

arXiv: https://arxiv.org/abs/2107.13507 · HTML: https://mlsanity.com/paper/2107.13507/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 37 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 24 of 24)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Reward learning from human preferences and demonstrations in Atari](https://mlsanity.com/paper/1811.06521/) (2018)
- [End-to-end Interpretable Neural Motion Planner](https://mlsanity.com/paper/2101.06679/) (2021)
- [Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1507.04888/) (2015)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [Batch Active Preference-Based Learning of Reward Functions](https://mlsanity.com/paper/1810.04303/) (2018)
- [Learning Reward Functions by Integrating Human Demonstrations and Preferences](https://mlsanity.com/paper/1906.08928/) (2019)
- [Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles](https://mlsanity.com/paper/1910.04586/) (2019)
- [Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks](https://mlsanity.com/paper/1902.09355/) (2019)
- [Watch This: Scalable Cost-Function Learning for Path Planning in Urban Environments](https://mlsanity.com/paper/1607.02329/) (2016)
- [Learning Preferences for Manipulation Tasks from Online Coactive Feedback](https://mlsanity.com/paper/1601.00741/) (2016)
- [Rule-based Optimal Control for Autonomous Driving](https://mlsanity.com/paper/2101.05709/) (2021)
- [Protest Activity Detection and Perceived Violence Estimation from Social Media Images](https://mlsanity.com/paper/1709.06204/) (2017)
- [Safety of the Intended Driving Behavior Using Rulebooks](https://mlsanity.com/paper/2105.04472/) (2021)
- [Interpretable Safety Validation for Autonomous Vehicles](https://mlsanity.com/paper/2004.06805/) (2020)
- [Towards a Skill- And Ability-Based Development Process for Self-Aware Automated Road Vehicles](https://mlsanity.com/paper/1708.02532/) (2017)
- [PlanIt: A Crowdsourcing Approach for Learning to Plan Paths from Large Scale Preference Feedback](https://mlsanity.com/paper/1406.2616/) (2014)
- [Priority-based coordination of autonomous and legacy vehicles at intersection](https://mlsanity.com/paper/1407.5813/) (2014)
- [ChaLearn Looking at People: A Review of Events and Resources](https://mlsanity.com/paper/1701.02664/) (2017)
- [Towards Assume-Guarantee Profiles for Autonomous Vehicles](https://mlsanity.com/paper/1909.04850/) (2019)
- [Rules of the Road: Safety and Liveness Guarantees for Autonomous Vehicles](https://mlsanity.com/paper/2011.14148/) (2020)

## Built on by (most cited first, 12 of 12)

- [Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer](https://mlsanity.com/paper/2207.14024/) (2022)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future](https://mlsanity.com/paper/2312.03408/) (2023)
- [A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving](https://mlsanity.com/paper/2405.01440/) (2024)
- [Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles](https://mlsanity.com/paper/2212.03323/) (2022)
- [Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap](https://mlsanity.com/paper/2404.00019/) (2024)
- [Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors](https://mlsanity.com/paper/2307.01408/) (2023)
- [Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules](https://mlsanity.com/paper/2202.09269/) (2022)
- [RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts](https://mlsanity.com/paper/2405.11139/) (2024)
- [ScenicRules: An Autonomous Driving Benchmark with Multi-Objective Specifications and Abstract Scenarios](https://mlsanity.com/paper/2602.16073/) (2026)
- [Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2301.11153/) (2023)
- [Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Driving](https://mlsanity.com/paper/2603.20230/) (2026)

## Abstract (arXiv)

Autonomous vehicles must balance a complex set of objectives. There is no consensus on how they should do so, nor on a model for specifying a desired driving behavior. We created a dataset to help address some of these questions in a limited operating domain. The data consists of 92 traffic scenarios, with multiple ways of traversing each scenario. Multiple annotators expressed their preference between pairs of scenario traversals. We used the data to compare an instance of a rulebook, carefully hand-crafted independently of the dataset, with several interpretable machine learning models such as Bayesian networks, decision trees, and logistic regression trained on the dataset. To compare driving behavior, these models use scores indicating by how much different scenario traversals violate each of 14 driving rules. The rules are interpretable and designed by subject-matter experts. First, we found that these rules were enough for these models to achieve a high classification accuracy on the dataset. Second, we found that the rulebook provides high interpretability without excessively sacrificing performance. Third, the data pointed to possible improvements in the rulebook and the rules, and to potential new rules. Fourth, we explored the interpretability vs performance trade-off by also training non-interpretable models such as a random forest. Finally, we make the dataset publicly available to encourage a discussion from the wider community on behavior specification for AVs. Please find it at github.com/bassam-motional/Reasonable-Crowd.