NDST: Neural Driving Style Transfer for Human-Like Vision-Based Autonomous Driving
<|reference_start|>NDST: Neural Driving Style Transfer for Human-Like Vision-Based Autonomous Driving: Autonomous Vehicles (AV) and Advanced Driver Assistant Systems (ADAS) prioritize safety over comfort. The intertwining factors of safety and comfort emerge as pivotal elements in ensuring the effectiveness of Autonomous Driving (AD). Users often experience discomfort when AV or ADAS drive the vehicle on their behalf. Providing a personalized human-like AD experience, tailored to match users' unique driving styles while adhering to safety prerequisites, presents a significant opportunity to boost the acceptance of AVs. This paper proposes a novel approach, Neural Driving Style Transfer (NDST), inspired by Neural Style Transfer (NST), to address this issue. NDST integrates a Personalized Block (PB) into the conventional Baseline Driving Model (BDM), allowing for the transfer of a user's unique driving style while adhering to safety parameters. The PB serves as a self-configuring system, learning and adapting to an individual's driving behavior without requiring modifications to the BDM. This approach enables the personalization of AV models, aligning the driving style more closely with user preferences while ensuring baseline safety critical actuation. Two contrasting driving styles (Style A and Style B) were used to validate the proposed NDST methodology, demonstrating its efficacy in transferring personal driving styles to the AV system. Our work highlights the potential of NDST to enhance user comfort in AVs by providing a personalized and familiar driving experience. The findings affirm the feasibility of integrating NDST into existing AV frameworks to bridge the gap between safety and individualized driving styles, promoting wider acceptance and improved user experiences.<|reference_end|>
@article{kim2024ndst:,
    title={NDST: Neural Driving Style Transfer for Human-Like Vision-Based
  Autonomous Driving},
    author={Donghyun Kim, Aws Khalil, Haewoon Nam, Jaerock Kwon},
    journal={arXiv preprint arXiv:2407.08073},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08073},
    primaryClass={cs.RO cs.AI cs.LG}
}