Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles
<|reference_start|>Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles: With the increasing presence of autonomous vehicles (AVs) on public roads, developing robust control strategies to navigate the uncertainty of human-driven vehicles (HVs) is crucial. This paper introduces an advanced method for modeling HV behavior, combining a first-principles model with Gaussian process (GP) learning to enhance velocity prediction accuracy and provide a measurable uncertainty. We validated this innovative HV model using real-world data from field experiments and applied it to develop a GP-enhanced model predictive control (GP-MPC) strategy. This strategy aims to improve safety in mixed vehicle platoons by integrating uncertainty assessment into distance constraints. Comparative simulation studies with a conventional model predictive control (MPC) approach demonstrated that our GP-MPC strategy ensures more reliable safe distancing and fosters efficient vehicular dynamics, achieving notably higher speeds within the platoon. By incorporating a sparse GP technique in HV modeling and adopting a dynamic GP prediction within the MPC framework, we significantly reduced the computation time of GP-MPC, marking it only 4.6% higher than that of the conventional MPC. This represents a substantial improvement, making the process about 100 times faster than our preliminary work without these approximations. Our findings underscore the effectiveness of learning-based HV modeling in enhancing both safety and operational efficiency in mixed-traffic environments, paving the way for more harmonious AV-HV interactions.<|reference_end|>
@article{wang2024enhancing,
    title={Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient
  Control of Autonomous and Human-Driven Vehicles},
    author={Jie Wang, Yash Vardhan Pant, Lei Zhao, Micha{l} Antkiewicz, Krzysztof
  Czarnecki},
    journal={arXiv preprint arXiv:2404.06732},
    year={2024},
    doi={10.1109/TITS.2024.3384050},
    archivePrefix={arXiv},
    eprint={2404.06732},
    primaryClass={cs.RO cs.LG cs.SY eess.SY}
}