  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.
