# A Rule-Based Behaviour Planner for Autonomous Driving

arXiv: https://arxiv.org/abs/2407.00460 · HTML: https://mlsanity.com/paper/2407.00460/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 29 Jun 2024.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 6 of 6)

- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Rule-based Optimal Control for Autonomous Driving](https://mlsanity.com/paper/2101.05709/) (2021)
- [Learning Structured Declarative Rule Sets -- A Challenge for Deep Discrete Learning](https://mlsanity.com/paper/2012.04377/) (2020)

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

- [HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder](https://mlsanity.com/paper/2503.08612/) (2025)
- [ResWorld: Temporal Residual World Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2602.10884/) (2026)
- [RealDrive: Retrieval-Augmented Driving with Diffusion Models](https://mlsanity.com/paper/2505.24808/) (2025)
- [OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model](https://mlsanity.com/paper/2606.30421/) (2026)
- [Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey](https://mlsanity.com/paper/2501.01886/) (2025)
- [A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles](https://mlsanity.com/paper/2508.00917/) (2025)
- [Timed Rule-Based Supervision of an End-to-End Autonomous Parking Policy](https://mlsanity.com/paper/2609.31773/) (2026)

## Abstract (arXiv)

Autonomous vehicles require highly sophisticated decision-making to determine their motion. This paper describes how such functionality can be achieved with a practical rule engine learned from expert driving decisions. We propose an algorithm to create and maintain a rule-based behaviour planner, using a two-layer rule-based theory. The first layer determines a set of feasible parametrized behaviours, given the perceived state of the environment. From these, a resolution function chooses the most conservative high-level maneuver. The second layer then reconciles the parameters into a single behaviour. To demonstrate the practicality of our approach, we report results of its implementation in a level-3 autonomous vehicle and its field test in an urban environment.