# Real-Time Capable Decision Making for Autonomous Driving Using Reachable Sets

arXiv: https://arxiv.org/abs/2309.12289 · HTML: https://mlsanity.com/paper/2309.12289/

## Key facts

- Cited by 5 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 5 of its 49 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## 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, 5 of 5)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Optimization-Based Collision Avoidance](https://mlsanity.com/paper/1711.03449/) (2017)
- [Interaction-Aware Probabilistic Behavior Prediction in Urban Environments](https://mlsanity.com/paper/1804.10467/) (2018)
- [Spatiotemporal motion planning with combinatorial reasoning for autonomous driving](https://mlsanity.com/paper/2207.04418/) (2022)

## Built on by (most cited first, 5 of 5)

- [A Survey of World Models for Autonomous Driving](https://mlsanity.com/paper/2501.11260/) (2025)
- [PKRD-CoT: A Unified Chain-of-thought Prompting for Multi-Modal Large Language Models in Autonomous Driving](https://mlsanity.com/paper/2412.02025/) (2024)
- [Traffic-Rule-Compliant Trajectory Repair via Satisfiability Modulo Theories and Reachability Analysis](https://mlsanity.com/paper/2412.15837/) (2024)
- [Diffusion-FS: Multimodal Free-Space Prediction via Diffusion for Autonomous Driving](https://mlsanity.com/paper/2507.18763/) (2025)
- [Reachable Sets-based Trajectory Planning Combining Reinforcement Learning and iLQR](https://mlsanity.com/paper/2503.17398/) (2025)

## Abstract (arXiv)

Despite large advances in recent years, real-time capable motion planning for autonomous road vehicles remains a huge challenge. In this work, we present a decision module that is based on set-based reachability analysis: First, we identify all possible driving corridors by computing the reachable set for the longitudinal position of the vehicle along the lanelets of the road network, where lane changes are modeled as discrete events. Next, we select the best driving corridor based on a cost function that penalizes lane changes and deviations from a desired velocity profile. Finally, we generate a reference trajectory inside the selected driving corridor, which can be used to guide or warm start low-level trajectory planners. For the numerical evaluation we combine our decision module with a motion-primitive-based and an optimization-based planner and evaluate the performance on 2000 challenging CommonRoad traffic scenarios as well in the realistic CARLA simulator. The results demonstrate that our decision module is real-time capable and yields significant speed-ups compared to executing a motion planner standalone without a decision module.