# Lab2Car: A Versatile Wrapper for Deploying Experimental Planners in Complex Real-world Environments

arXiv: https://arxiv.org/abs/2409.09523 · HTML: https://mlsanity.com/paper/2409.09523/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2024.

## Built on by (most cited first, 2 of 2)

- [Explainable deep learning improves human mental models of self-driving cars](https://mlsanity.com/paper/2411.18714/) (2024)
- [TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning](https://mlsanity.com/paper/2509.13579/) (2025)

## Abstract (arXiv)

Human-level autonomous driving is an ever-elusive goal, with planning and decision making -- the cognitive functions that determine driving behavior -- posing the greatest challenge. Despite a proliferation of promising approaches, progress is stifled by the difficulty of deploying experimental planners in naturalistic settings. In this work, we propose Lab2Car, an optimization-based wrapper that can take a trajectory sketch from an arbitrary motion planner and convert it to a safe, comfortable, dynamically feasible trajectory that the car can follow. This allows motion planners that do not provide such guarantees to be safely tested and optimized in real-world environments. We demonstrate the versatility of Lab2Car by using it to deploy a machine learning (ML) planner and a classical planner on self-driving cars in Las Vegas. The resulting systems handle challenging scenarios, such as cut-ins, overtaking, and yielding, in complex urban environments like casino pick-up/drop-off areas. Our work paves the way for quickly deploying and evaluating candidate motion planners in realistic settings, ensuring rapid iteration and accelerating progress towards human-level autonomy.