Advanced Driver Assistance Systems (ADAS) have achieved significant progress
worldwide, with the primary goals of enhancing driving safety, improving
operational efficiency, and supporting vehicle automation. These systems are
increasingly dependent on intelligent connected technologies, which enhance
drivers' awareness and capacity to recognize and respond to potential road
hazards in real-time. Within ADAS, risk visualization systems have become
especially crucial, as they provide immediate alerts, thereby promoting safer
driving behavior and enabling drivers to make more informed decisions on the
road. This study expands upon existing frameworks by investigating the adoption
of advanced risk visualization systems among Chinese ride-hailing drivers
through an improved Unified Theory of Acceptance and Use of Technology (UTAUT2)
model. The improved model introduces two novel constructs: Technology Trust and
Perceived Risk, addressing critical gaps in understanding safety-critical
technology adoption. Using a structured questionnaire, data were collected from
774 Chinese ride-hailing drivers, focusing on elements such as performance
expectancy, ease of use, available support, pricing value, trust in technology,
and perceived risk. Additionally, the study assesses how factors like age,
gender, years of driving experience, and familiarity with technology may
moderate these adoption intentions. The results reveal that facilitating
conditions and price value have a strong positive effect on adoption intentions,
while perceived risk discourages adoption. While demographic factors such as
age, gender, and driving experience exert less influence, they remain
statistically significant. Based on these findings, this study proposes
practical strategies to enhance adoption, including improving technical support,
offering targeted training, implementing appealing pricing strategies, and
strengthening risk management. These strategies aim to mitigate barriers to
adoption and foster trust, promoting broader adoption of risk visualization
systems to advance safety and efficiency in the ride-hailing industry.