Lab2Car: A Versatile Wrapper for Deploying Experimental Planners in Complex Real-world Environments
<|reference_start|>Lab2Car: A Versatile Wrapper for Deploying Experimental Planners in Complex Real-world Environments: 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 search-based 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.<|reference_end|>
@article{heim2024lab2car:,
    title={Lab2Car: A Versatile Wrapper for Deploying Experimental Planners in
  Complex Real-world Environments},
    author={Marc Heim, Francisco Suarez-Ruiz, Ishraq Bhuiyan, Bruno Brito, Momchil
  S. Tomov},
    journal={arXiv preprint arXiv:2409.09523},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09523},
    primaryClass={cs.RO}
}