Lab2Car: A Versatile Wrapper for Deploying Experimental Planners in Complex Real-World Environments¶
Authors: Marc Heim, Francisco Suarez-Ruiz, Ishraq Bhuiyan, Bruno Brito, Momchil S. Tomov
Published: 2024 (Conference Paper)
Source: IEEE International Conference on Robotics and Automation (ICRA)
Algorithm: Lab2Car
arXiv: 2409.09523
DOI: 10.1109/ICRA55743.2025.11128008
Summary¶
Optimization-based wrapper that converts any planner's trajectory sketch into a safe, comfortable, dynamically feasible trajectory, enabling rapid real-world testing of experimental ML and classical planners on self-driving vehicles without full safety-stack integration.
Abstract¶
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.
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Tags¶
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Motion planning
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Autonomous driving
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Trajectory optimization
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Safety
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Real-world deployment
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Experimentation