# Don't Get Stuck: A Deadlock Recovery Approach

arXiv: https://arxiv.org/abs/2408.10167 · HTML: https://mlsanity.com/paper/2408.10167/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2024.

## Built on by (most cited first, 3 of 3)

- [Predictive Planner for Autonomous Driving with Consistency Models](https://mlsanity.com/paper/2502.08033/) (2025)
- [Large Language Model-assisted Autonomous Vehicle Recovery from Immobilization](https://mlsanity.com/paper/2510.26023/) (2025)
- [Your Ride, Your Rules: Psychology and Cognition Enabled Automated Driving Systems](https://mlsanity.com/paper/2506.11842/) (2025)

## Abstract (arXiv)

When multiple agents share space, interactions can lead to deadlocks, where no agent can advance towards its goal. This paper addresses this challenge with a deadlock recovery strategy. In particular, the proposed algorithm integrates hybrid-A^star, STL, and MPPI frameworks. Specifically, hybrid-A^star generates a reference path, STL defines a goal (deadlock avoidance) and associated constraints (w.r.t. traffic rules), and MPPI refines the path and speed accordingly. This STL-MPPI framework ensures system compliance to specifications and dynamics while ensuring the safety of the resulting maneuvers, indicating a strong potential for application to complex traffic scenarios (and rules) in practice. Validation studies are conducted in simulations and on scaled cars, respectively, to demonstrate the effectiveness of the proposed algorithm.