Behavior Cloning for Mini Autonomous Car Path Following
<|reference_start|>Behavior Cloning for Mini Autonomous Car Path Following: This article presents the implementation and evaluation of a behavior cloning approach for route following with autonomous cars. Behavior cloning is a machine-learning technique in which a neural network is trained to mimic the driving behavior of a human operator. Using camera data that captures the environment and the vehicle's movement, the neural network learns to predict the control actions necessary to follow a predetermined route. Mini-autonomous cars, which provide a good benchmark for use, are employed as a testing platform. This approach simplifies the control system by directly mapping the driver's movements to the control outputs, avoiding the need for complex algorithms. We performed an evaluation in a 13-meter sizer route, where our vehicle was evaluated. The results show that behavior cloning allows for a smooth and precise route, allowing it to be a full-sized vehicle and enabling an effective transition from small-scale experiments to real-world implementations.<|reference_end|>
@article{moraes2024behavior,
    title={Behavior Cloning for Mini Autonomous Car Path Following},
    author={Pablo Moraes, Christopher Peters, Hiago Sodre, William Moraes,
  Sebastian Barcelona, Juan Deniz, Victor Castelli, Bruna Guterres, Ricardo
  Grando},
    journal={arXiv preprint arXiv:2410.07209},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07209},
    primaryClass={cs.RO}
}