Hybrid Imitation-Learning Motion Planner for Urban Driving
<|reference_start|>Hybrid Imitation-Learning Motion Planner for Urban Driving: With the release of open source datasets such as nuPlan and Argoverse, the research around learning-based planners has spread a lot in the last years. Existing systems have shown excellent capabilities in imitating the human driver behaviour, but they struggle to guarantee safe closed-loop driving. Conversely, optimization-based planners offer greater security in short-term planning scenarios. To confront this challenge, in this paper we propose a novel hybrid motion planner that integrates both learning-based and optimization-based techniques. Initially, a multilayer perceptron (MLP) generates a human-like trajectory, which is then refined by an optimization-based component. This component not only minimizes tracking errors but also computes a trajectory that is both kinematically feasible and collision-free with obstacles and road boundaries. Our model effectively balances safety and human-likeness, mitigating the trade-off inherent in these objectives. We validate our approach through simulation experiments and further demonstrate its efficacy by deploying it in real-world self-driving vehicles.<|reference_end|>
@article{gariboldi2024hybrid,
    title={Hybrid Imitation-Learning Motion Planner for Urban Driving},
    author={Cristian Gariboldi, Matteo Corno, Beng Jin},
    journal={arXiv preprint arXiv:2409.02871},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02871},
    primaryClass={cs.RO cs.AI cs.LG}
}